Method and system for detecting a threat or other suspicious activity in the vicinity of a stopped emergency vehicle

A video processing system (VPS) receives video data from at least one video camera included with an emergency vehicle. The VPS also receives audio data from at least one microphone operating in conjunction with the video camera(s). The VPS determines whether received data representing a first set of video frames includes a predefined image pattern. If so, the VPS tracks the image pattern within the video data and determines whether data representing a second set of later-in-time video frames includes the tracked image pattern. If the second set of video frames includes the tracked image pattern, the VPS determines whether the tracked image pattern has changed position suspiciously. If so, the VPS communicates an alert. The VPS also determines whether received audio data includes a predefined audio pattern. If so, the VPS inserts a digital marker within the video data at the time at which the audio pattern commenced.

TECHNICAL FIELD

The present disclosure relates generally to video-based monitoring systems and, more particularly, to methods and systems for detecting threats or other suspicious activity using real-time or near real-time video data analysis.

BACKGROUND

Mobile and fixed video surveillance systems are well known. Such systems are regularly utilized for a variety of reasons, including to monitor the activities and surroundings of employees of package delivery service companies and cash transport service companies, as well as to monitor activities within banks and stores, at automated teller machines (ATMs), and in the vicinities of public safety or law enforcement personnel. Most existing surveillance systems record video over a period of time and then store the video to a separate external memory device or to internal memory for later viewing. Where memory for storing surveillance video is limited in size, such memory may become full prior to storing new video or during the storage of new video. In such a case, the new video may be stored by overwriting the oldest stored video, such that video data for a most recent chosen time period is always stored in memory for later viewing.

Some business and government video surveillance systems, such as those in casinos or prisons, are monitored in real time by employees or contractors of the business or government. Such systems are costly to operate due to the need for regular or continual human interaction.

Other video surveillance systems are not configured to facilitate real-time human monitoring and instead store video for later viewing as discussed above. Such systems include law enforcement systems containing in-vehicle and/or body cameras. Few, if any, of such video surveillance systems perform real-time or near real-time object tracking and automated threat or suspicious activity notification based thereon.

SUMMARY

Generally, the present disclosure relates to a method and system for detecting suspicious activity, including a potential threat, in a vicinity of a non-moving emergency vehicle. According to one exemplary embodiment, a video processing system receives video data in real time or near real time from at least one video camera included with the emergency vehicle. The video data includes data representing a plurality of time-sequenced video frames. The video camera or cameras capture images of at least one video capture area proximate the emergency vehicle. In addition to receiving video data, the video processing system receives audio data in real time or near real time from at least one microphone operating in conjunction with the video camera or cameras. The audio data is time-synchronized with the video data on a video frame-by-video frame basis.

Responsive to receiving the video data, the video processing system determines whether data representing a first set of received video frames includes data representing one or more predefined image patterns. If the first set of video frames includes data representing the one or more predefined image patterns, the video processing system tracks the one or more predefined image patterns within the video data to produce one or more tracked image patterns. Responsive to receiving the audio data, the video processing system determines whether the audio data includes data representing one or more predefined audio patterns. If the audio data includes a predefined audio pattern, the video processing system inserts a digital marker within the video data at a time at which the predefined audio pattern commenced.

After image pattern tracking has begun, the video processing system determines whether data representing a second set of received video frames includes data representing the tracked image pattern or patterns, where images in the second set of video frames were captured later in time than images in the first set of video frames. Responsive to determining that the data representing the second set of video frames includes data representing the one or more tracked image patterns, the video processing system determines whether the one or more tracked image patterns have changed position in a suspicious manner. Responsive to determining that the one or more tracked image patterns have changed position in a suspicious manner, the video processing system communicates an alert (e.g., to the person being monitored).

According to one exemplary embodiment, a system for detecting suspicious activity in a vicinity of a non-moving emergency vehicle includes at least one video camera, at least one microphone, and a video processing apparatus. The one or more video cameras are included with the emergency vehicle and positioned to capture images of respective video capture areas proximate the emergency vehicle. The one or more cameras are also configured to output video data representing the captured images. The one or more microphones operate in conjunction with the one or more video cameras.

The video processing apparatus is communicatively coupled to the one or more video cameras and the one or more microphones, and includes at least one communication interface and a video processor. The one or more communication interfaces of the video processing apparatus are operable to receive video data in real time or near real time from the one or more video cameras, where the video data from each camera includes data representing a plurality of time-sequenced video frames. The one or more communication interfaces of the video processing apparatus are also operable to receive audio data in real time or near real time from the one or more microphones, where the audio data is time-synchronized with the video data on a video frame-by-video frame basis.

The video processor is operably coupled to the one or more communication interfaces and operable in accordance with a set of operating instructions to perform several functions. For example, the video processor determines, from the video data, whether data representing a first set of received video frames includes data representing one or more predefined image patterns. Additionally, the video processor determines, from the audio data, whether the audio data includes data representing one or more predefined audio patterns. If the data representing the first set of video frames includes data representing the one or more predefined image patterns, the video processor tracks the one or more predefined image patterns within the video data to produce one or more tracked image patterns. If the audio data includes a predefined audio pattern, the video processor inserts a digital marker within the video data at a time at which the predefined audio pattern commenced.

After image pattern tracking has begun, the video processor determines, from the video data, whether data representing a second set of received video frames includes data representing the one or more tracked image patterns, where images in the second set of video frames were captured later in time than images in the first set of video frames. Responsive to determining that the data representing the second set of video frames includes data representing the one or more tracked image patterns, the video processor determines whether the one or more tracked image patterns have changed position in a suspicious manner. Responsive to determining that the one or more tracked image patterns have changed position in a suspicious manner, the video processor communicates an alert (e.g., to an operator or passenger of the emergency vehicle, or to an emergency management system).

Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale or to include every component of an element. For example, the dimensions of some of the elements in the figures may be exaggerated alone or relative to other elements, or some and possibly many components of an element may be excluded from the element, to help improve the understanding of the various embodiments of the present disclosure. Skilled artisans will also appreciate that the drawings are not intended to be comprehensive; thus, they may exclude elements and functions that would be readily apparent to those skilled in the art in order to implement the methods and systems described herein.

DETAILED DESCRIPTION

Detailed embodiments of video analysis-based threat detection methods and systems are disclosed herein; however, such embodiments are merely exemplary in nature. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but rather should be interpreted merely as a basis for the claims and as a representative basis for teaching one skilled in the art how to carry out the disclosed methods and systems in appropriate circumstances. Except as expressly noted, the terms and phrases used herein are not intended to be limiting, but rather are intended to provide an understandable description of the disclosed methods and systems.

Exemplary embodiments of the present disclosure can be more readily understood with reference toFIGS. 1-26, in which like reference numerals designate like items.FIG. 1is an electrical block diagram of a video processing system100in accordance with an exemplary embodiment of the present disclosure. According to this embodiment, the video processing system100includes, inter alia, one or more cameras101-104(four shown for illustration) and a video processing apparatus106. The video processing apparatus106may include, inter alia, a communication interface108, a video processor110, and an optional memory114.

The cameras101-104are preferably commercially-available, digital, high-definition cameras, such as panoramic cameras available from 360fly, Inc. of Fort Lauderdale, Fla., but may also or alternatively be any high definition security cameras with the capability to communicate video data over one or more communication networks. Where one of the cameras (e.g., camera101) or the only camera is intended to be secured to a body of a person under surveillance, the camera101may be a low profile, wide-angle, panoramic camera, such as the panoramic camera disclosed in U.S. Patent Application Publication No. US 20170195563 A1, which publication is incorporated herein by this reference. Additionally, where one or more of the cameras (e.g., cameras101,102) are secured to a person's body, a vehicle, or other movable object, the cameras101,102may include one or more types of motion sensors, such as two-axis or three-axis accelerometers, gyroscopes, magnetometers, GPS units, and/or composite inertial measurement units. Where the cameras101-104are positioned apart from the video processing apparatus106, the cameras101-104may further include communication circuitry sufficient to communicate video data and optional motion data (e.g., sensor data) over wireless and/or wired networks to the video processing apparatus106. Where a camera101-104is collocated with the video processing apparatus106, the camera101-104may include one or more data buses or other commutation paths to communicate video data and optional motion data (e.g., sensor data) to the video processing apparatus106.

With regard to the video processing apparatus106, the communication interface108includes antennas, filters, amplifiers, transceivers, modems, transcoders, and any other hardware and/or software necessary to facilitate communication between the cameras101-104and the video processor110over known or future-developed wired or wireless networks. Such networks may include Wi-Fi (IEEE 802.11 a/b/g/n/ac); WiMAX (IEEE 802.16); 3G (CDMA, GSM), 4G LTE, and 5G cellular networks; and/or Ethernet. The communication interface108provides communicative coupling between the video processing apparatus106and the cameras101-104.

The video processor110is operably coupled to the communication interface108and may be any digital video processor or combination of digital video processors capable of decoding, analyzing, and otherwise processing video data and optional sensor data received from the cameras101-104. Where the video processing apparatus106is operable to communicate video data or augmented video data to a wireless communication device carried by a person under surveillance, such as a smartphone, tablet computer, personal digital assistant-type device, or other handheld mobile device, the video processor110may further include capability to encode video data for viewing on such a device. According to one exemplary embodiment, the video processor110is implemented as a system on a chip (SoC) programmed to execute a video codec and real-time communication protocols, as well as perform other processing functions on video data and optional sensor data received from the cameras101-104in accordance with various embodiments of the present disclosure.

Where the video processor110does not include onboard memory or includes an inadequate amount of onboard memory for purposes of carrying out all of its functions in accordance with the present disclosure (e.g., where the video processor110includes onboard memory to store firmware, but not application software), the video processing apparatus106may include separate memory114to meet the operational requirements of the video processing apparatus106. The memory114may store executable code that contains the operating instructions for the video processor110, as well as store video data, motion data, or other data used during video processing or desired for later retrieval. The memory114may include volatile memory (such as random access memory (RAM)) and non-volatile memory (such as various types of read only memory (ROM)).

Where the video processing apparatus106is collocated with a local alerting mechanism112, such mechanism112may include an audio speaker, a horn, a haptic or tactile alerting device, one or more lights or lighting units, and/or a video display. The local alerting mechanism112is intended to quickly alert the person under surveillance as to the presence of a possible threat when the video processing apparatus110, as part of the overall video processing system100, determines from received video data (and optionally motion data) that such a potential threat is present. Where a local alerting mechanism is not present or desired, the video processor110may communicate an alert signal to a remote alerting device, such as a wireless communication device carried by the person under surveillance, by way of the communication interface108.

Operation of video processing systems, such as video processing system100, will be described below in connection withFIGS. 2-7. An optional cloud-based implementation of the video processing apparatus106is described below in connection withFIG. 8.

Referring now toFIG. 2, there is shown a process flow diagram200of steps executed by a video processing system to detect a threat to a person based on real-time or near real-time video analysis in accordance with an exemplary embodiment of the present disclosure. The steps of the process flow diagram200may be performed by the video processing system (and primarily by its video processor) through execution of stored operating instructions (firmware and/or software). By way of example, but not limitation, the threat detection process flow ofFIG. 2is described below with reference to the video processing system100ofFIG. 1.

The process flow begins when one or more cameras101-104capture images within video capture areas defined by the cameras' respective fields of view. The cameras101-104generate encoded video data streams from the images and divide the video streams into a series of time-sequenced or time-stamped video frames according to the video streaming protocol being used. In one exemplary embodiment, the camera or cameras101-104are configured to capture images and encode video data at a rate of at least 30 frames per second. The video streams are communicated to the video processing apparatus106for video analysis processing.

The cameras' fields of view are such that the cameras' video capture areas are proximate the location of the person under surveillance when the threat detection process is being executed. For example, one camera101may be a low profile or other style body camera secured to the front or back of the person under surveillance, such as through use of a strap or belt, vest, holster, or other device. Such a camera101may, depending on its capabilities, capture images extending out several feet or meters (e.g., 150 feet or 50 meters or more) as referenced from the person's position.

Another one or more cameras102-104may be mounted at predetermined locations on a vehicle (e.g., truck, car, boat, bus, motorcycle, and so forth) that transported the person to his or her current location or that is otherwise positioned near the person under surveillance. The positioning of the cameras102-104on the vehicle may be such that the cameras102-104captures images of the person and his surroundings at locations where the person is expected to be after stopping the vehicle. For example, where the person is a courier for a package delivery service company or a security guard for a cash management or transport service company, the vehicle-mounted cameras102-104may be mounted to the vehicle at multiple locations, such as the driver's side of the vehicle (e.g., adjacent the driver's side door or on the driver's side of the hood), the passenger's side of the vehicle, and/or the back of the vehicle (e.g., above and/or adjacent to the rear doors). Depending on the types of cameras102-104utilized, the cameras102-104may capture images extending out several feet or meters (e.g., 150 feet or 50 meters or more) from the vehicle.

Other cameras may be mounted at fixed locations near the location of the person. For example, cameras may be mounted to buildings, canopies, trees, or other objects, or within structures (e.g., within an ATM) at the general location of the person. Due to their positioning, such cameras may capture images within a much wider video capture area than the video capture areas of body-mounted or vehicle-mounted cameras.

The video processing apparatus106receives (201) a video data stream from each camera101-104in real time or near real time via the apparatus' communication interface108. In other words, each camera101-104captures images, encodes the images into video data containing time-sequenced video frames, and communicates the video data to the video processing apparatus106as a stream of video frames in accordance with a video streaming protocol, without intentionally delaying the flow of video data any more than is necessary. That is, neither the video processing apparatus106nor the video processing system100as a whole introduces any delays other than normal processing and communication delays. Use of the terms “real time,” “real-time,” “near real-time,” and “near real time” take into account such inherent delays. The video processor110may use one or more video streaming control protocols, such as version 2.0 of the Real Time Streaming Protocol (RTSP 2.0) or any successor thereof as standardized by the Internet Engineering Task Force (IETF) or another standards body, to control the delivery of video data from the cameras101-104. According to one exemplary embodiment, the cameras101-104and the video processor110use video transport and streaming protocols, such as the Real-Time Messaging Protocol (RTMP) and the Real-Time Transport Protocol (RTP) or any successors thereof as standardized by the IETF or another standards body, to transmit and receive video data in real time or near real time.

As the video data from a particular camera101-104is received at the video processor110, the video processor110extracts (203) data representing a video frame from the video data based on the video streaming protocol and the video codec (e.g., H.264 or H.265) used by the camera101-104and the video processor110, and determines (205) whether the video frame data includes data representative of one or more predefined patterns. For example, the video processor110may compare portions of the video frame data to data representative of a set of predefined, potential threat patterns previously stored in memory114to determine whether the video frame or any portion thereof includes data substantially similar to data representative of a potential threat pattern. Video data may be considered substantially similar to pattern data where the video data has at least a fifty percent (50%) correspondence with the pattern data. Additionally or alternatively, the video processor110may execute machine learning and computer vision algorithms to perform object detection, face detection, face recognition, summarization, threat detection, natural language processing, sentiment analysis, traffic monitoring, intention detection and so on to evaluate whether the video frame data includes data representative of one or more predefined patterns.

The set of predefined patterns may include, for example, the outline or other features of a human body or a portion thereof, the outline or other features of one or more predetermined objects (such as a firearm, knife, bat, club, TASER, or other object that could be used as a weapon), the outline or other features of a vehicle, and/or the features of one or more types of locations. The video processor110may be programmed to update and/or expand the stored threat pattern data by applying machine learning techniques, such as supervised learning techniques (e.g., pattern recognition, object classification, and/or regression algorithms), unsupervised learning techniques (e.g., association, clustering, and/or dimensionality reduction algorithms), and/or reinforcement learning techniques, to video data received by the video processor110over time.

Where the video processing apparatus106receives video data streams from multiple cameras101-104, the video processor110analyzes each video stream separately and may use metadata within the video streams to time-synchronize the streams. The metadata for each video data stream may include a time-and-date stamp, which permits the video processor110to align the video frames of the video data streams even though such streams may be received at different times by the video processing apparatus106.

When the video frame data from a particular camera101-104does not include data representative of a predefined pattern, the video processor110extracts (207) data representing the next video frame from the video data stream and determines (205) whether that video frame data includes data representative of one or more of the predefined patterns. When the video frame data from a particular camera includes data representative of at least one predefined pattern (e.g., a pattern match or correspondence occurs), the video processor110commences (209) tracking of the detected pattern or patterns within the video data and extracts (211) data representing one or more subsequent video frames from the video data stream.

According to one exemplary embodiment, pattern tracking continues for a predetermined period of time over a predetermined set of subsequent video frames, which period may be extended by the video processor110based on pre-established extension criteria. The set of subsequent video frames may include contiguous video frames, periodically positioned video frames (e.g., every other video frame in the set, every third video frame in the set, and so forth), or randomly selected video frames within the tracking time period. For example, where the video data was captured by the camera101-104at 30 frames per second, pattern tracking may continue for a fraction of a second (e.g., 333 milliseconds or 500 milliseconds) or for multiple seconds as may be selected by the system operator. As a further example, where pattern tracking is to be performed on contiguous video frames for a period of 500 milliseconds after a pattern has been detected and the video data includes 30 frames per second, pattern tracking may be programmed to occur for data representing fifteen consecutive video frames.

The video processor110analyzes the data representing the set of one or more subsequent video frames and determines (213) whether that video frame data includes data representative of the tracked pattern or patterns (e.g., determines whether any portion of the video frame data in the tracked video frames is substantially similar to the tracked pattern or patterns). If a tracked pattern is found in the data representing the set of subsequent video frames, the video processor110determines (215) whether the tracked pattern is positioned suspiciously relative to the position of the person under surveillance. Otherwise, the video processor110extracts (203) the next video frame from the video data and the process repeats.

To determine whether the tracked pattern is positioned suspiciously, the video processor110may determine a motion vector (e.g., velocity) for the tracked pattern based on the video frame data and, responsive thereto, determine whether the motion vector is on a track to intercept or pass closely to the person under surveillance. For example, by analyzing video data from a camera (e.g., camera102) positioned other than on the person under surveillance's body, the video processor110may initially (e.g., at block205) detect a potential threat pattern, as well as the pattern of the person under surveillance. The video processor110may thereafter commence pattern tracking and compute a velocity of the tracked pattern and a velocity of the person under surveillance over the tracking period. If the tracked pattern and person are projected to intercept at a threshold time in the future (e.g., within five seconds), the video processor10may determine that the tracked pattern is positioned suspiciously relative to the person under surveillance. Alternatively, by analyzing video data from a camera (e.g., camera101) positioned on the person's body, the video processor110may determine that the tracked pattern is approaching the person under surveillance, which may be deemed a suspicious positioning of the tracked pattern depending on other factors, such the position and rate of approach, and/or the presence of another predefined pattern in the video data (e.g., the pattern for a weapon). One exemplary process for determining whether a tracked pattern is positioned suspiciously relative to the position of a person under surveillance is described below with respect toFIG. 3. Another exemplary process for determining whether a tracked pattern is positioned suspiciously relative to the position of a person under surveillance based on analysis of video data from the person's body camera and from a nearby fixed-position or static camera is described below with respect toFIG. 4.

When the video processor110determines that one or more tracked patterns are positioned suspiciously relative to the position of the person under surveillance, the video processor110alerts (217) the person under surveillance as to a potential threat. For example, the video processor110may activate a local alert, such as activate an audible and/or visual alarm or send an audio message to a local sound speaker, to notify the person. Alternatively, the video processor110may communicate, via the communication interface108, an alert message to a mobile application executing on a wireless communication device carried by the person (e.g., smartphone, cellular phone, tablet computer, personal digital assistant). In the latter case, the alert message may cause the mobile application to activate an audible alarm and/or a haptic alarm of the wireless communication device to notify the person of the potential threat. Still further, the video processor110may communicate, via the communication interface108, at least some of the video data from the analyzed video stream (e.g., the last ten seconds or 300 video frames) to a mobile video processing and display application executing on a wireless communication device carried by the person. In this case, the mobile application may be configured to automatically play and display the received video to enable the person under surveillance to assess the potential threat and react thereto as necessary.

FIG. 3is a process flow diagram300of steps executed by a video processing system100(e.g., through operation of its video processor110) to determine whether a tracked pattern is positioned suspiciously relative to a position of a person under video surveillance, in accordance with one exemplary embodiment of the present disclosure. The process flow illustrated inFIG. 3may have particular applicability for analyzing video data supplied by a camera secured to the body of the person under surveillance.

According to the logic flow ofFIG. 3, the video processor110defines (301) a bounding area for the tracked pattern. The bounding area may be defined by a square, rectangle, oval, triangle, or other geometric shape positioned around the tracked pattern to form a trackable area for purposes of reducing the amount of processing resources necessary to track the pattern and its positioning relative to a position of the person under surveillance. In other words, each tracked pattern may be “bounded” within a predefined or adaptive virtual area to make pattern tracking less processing intensive.

In addition to defining a bounding area for each tracked pattern, the video processor110sets (303) the position of the person under surveillance as the reference origin for the video data stream being processed. Thus, the position of the person under surveillance is the reference point for all calculations and other determinations relevant to evaluating the positioning of the tracked pattern according to this exemplary embodiment.

Once the tracked pattern bounding area has been defined and the reference origin set, the video processor110determines (305) whether the tracked pattern bounding area is becoming larger and/or closer to the bottom of each image in the set of subsequent video frames that is subject to pattern tracking analysis. To determine whether the tracked pattern bounding area is becoming larger in the set of subsequent video frames, the video processor110may, according to an exemplary embodiment, determine a size of the tracked pattern bounding area in each video frame of the set of subsequent video frames. Based on such bounding area size data, the video processor110may determine a linear regression to model how the size of the tracked pattern bounding area (e.g., size of the pixel area) changes across the set of subsequent video frames. Thereafter, the video processor110may determine a gradient for the linear regression and compare the gradient to a threshold. When the gradient exceeds the threshold, the video processor110may determine that the tracked pattern bounding area is becoming larger over the subsequent video frames. Therefore, according to this exemplary embodiment, the video processor110may be programmed to use a simple or Bayesian linear technique to interpret the bounding area data captured over the set of subsequent video frames for the purpose of evaluating whether the tracked pattern bounding area is becoming larger over time. Those of ordinary skill in the art will readily recognize and appreciate that the video processor110may be programmed to use other known regression or statistical analysis techniques to evaluate how the size of the tracked pattern bounding area is changing over the set of subsequent video frames.

To determine whether the tracked pattern bounding area is becoming closer to a bottom of each image in the set of subsequent video frames, the video processor110may, according to an exemplary embodiment, determine a position of a coordinate along a bottom edge of the tracked pattern bounding area in each video frame of the set of subsequent video frames. The determined position may be a pixel position or an estimated physical position of the edge of the boundary area under an assumption that the boundary area actually existed in the real world. For example, the video processor110may determine a position of the center coordinate along the bottom edge of the tracked pattern bounding area, although the position of any coordinate along the bottom edge of the tracked pattern bounding area may suffice with appropriate angular correction applied, if necessary.

The video processor110may then use the bottom coordinate position data to determine a relationship (e.g., an estimated distance) between the position of the coordinate along the bottom edge of the tracked pattern bounding area and the reference origin for each video frame of the set of subsequent video frames. Based on such relationship, the video processing system may determine a linear regression to represent how the relationship between the position of the coordinate along the bottom edge of the tracked pattern bounding area and the reference origin changes across the set of subsequent video frames. For example, the video processor110may determine a distance (e.g., an estimated actual distance or pixel distance) between the position of the coordinate along the bottom edge of the tracked pattern bounding area and the reference origin for each video frame of the set of subsequent video frames and then determine a linear regression to model how the distance changes over time across the set of subsequent video frames.

The video processor110may further determine a gradient for the linear regression and compare the gradient, which may be negative, to a threshold. When the gradient is less than the threshold, the video processor110may determine that the tracked pattern bounding area is becoming closer to a bottom of each image in the set of subsequent video frames. Those of ordinary skill in the art will readily recognize and appreciate that the video processor110may be programmed to use other known regression or statistical analysis techniques to evaluate how the position of the tracked pattern bounding area is changing over the set of subsequent video frames. Additionally, those of ordinary skill in the art will readily recognize and appreciate that the video processor110may be programmed to use other position coordinates along another edge or edges of the tracked pattern bounding area in order assess whether the tracked pattern bounding area is becoming closer to a bottom of each image in the set of subsequent video frames. More detailed exemplary embodiments for using tracked pattern bounding area changes (or lack thereof) over multiple video frames to assist in the determination of whether a tracked pattern is positioned suspiciously relative to a person under surveillance are described below with respect toFIGS. 5-7.

When the video processor110determines that the tracked pattern bounding area is becoming larger and/or closer to the bottom of each image in the set of subsequent video frames that is subject to pattern tracking analysis, the video processor determines (307) that the tracked pattern is positioned suspiciously relative to the person under surveillance. On the other hand, when the video processor110determines that the tracked pattern bounding area is not becoming larger and/or closer to the bottom of each image in the set of subsequent video frames that is subject to pattern tracking analysis, the video processor determines (309) that the tracked pattern is not positioned suspiciously relative to the person under surveillance. Thus, according to this embodiment, the video processor110may determine that the tracked pattern is positioned suspiciously relative to the person under surveillance if the tracked pattern bounding area is becoming larger over the set of subsequent video frames, the tracked pattern is becoming closer to the bottom of each image over the set of subsequent video frames, or both. For example, if the tracked pattern is a pattern of a person, the bounding area is the area of a rectangle positioned around the tracked pattern, and the person is running toward the person under surveillance, the size of the tracked pattern bounding area will increase and a coordinate along the bottom edge of the tracked pattern bounding area will become closer to a bottom of each image over the set of subsequent video frames indicating suspicious positioning of the tracked pattern. On the other hand, if the tracked pattern is the pattern of a drone, the bounding area is the area of a rectangle positioned around the tracked pattern, and the drone is flying toward the person under surveillance while also increasing in altitude, the size of the tracked pattern bounding area may not increase over the set of subsequent video frames, but a coordinate along the bottom edge of the tracked pattern bounding area will become closer to a bottom of each image over the set of subsequent video frames. In this case, movement of the drone toward the person under surveillance results in the tracked pattern bounding area becoming closer to a bottom of each image in the subsequent video frames, thereby indicating suspicious positioning of the tracked pattern relative to the person under surveillance.

FIG. 4is a process flow diagram400of steps executed by a video processing system100to detect a threat to a person based on real-time or near real-time analysis of video data supplied by multiple cameras in accordance with a further exemplary embodiment of the present disclosure. According to this embodiment, the video processing system100, through operation of its communication interface108and video processor110, receives (401) video data streams from a camera secured to the body of the person under surveillance and one or more statically-positioned cameras. The statically-positioned cameras may be mounted to or within one or more objects, such as a vehicle, a light pole, an awning or canopy, a structural support pole, a telephone pole, a tree, an automated teller machine (ATM), or any other object. The video processor110may also be programmed to use a streaming control protocol, such as RTSP, to control the video data streams from the multiple cameras.

As each video data stream is received at the video processor110, the video processor110extracts (403) data representing a video frame from the video data based on the video streaming protocol and the video codec used by the camera101-104and the video processor110, and determines (405) whether the video frame data includes data representative of one or more predefined patterns. As discussed above with respect toFIG. 1, the video processor110may compare portions of the video frame data to data representative of a set of predefined, potential threat patterns previously stored in memory114to determine whether the video frame or any portion thereof includes data substantially similar to data representative of a potential threat pattern.

When the video frame data from a particular camera101-104does not include data representative of a predefined pattern, the video processor110extracts (407) data representing the next video frame from the video data stream and determines (405) whether that video frame data includes data representative of one or more of the predefined patterns. When the video frame data from a particular camera includes data representative of at least one predefined pattern, the video processor110commences (409) tracking of the detected pattern or patterns within the video data and extracts (411) data representing one or more subsequent video frames from the video data stream.

According to one exemplary embodiment, tracking continues for a predetermined period of time over a predetermined set of subsequent video frames, which period may be extended by the video processor110based on pre-established extension criteria. The set of subsequent video frames may include contiguous video frames, periodically positioned video frames (e.g., every other video frame in the set, every third video frame in the set, and so forth), or randomly selected video frames within the tracking time period. The video processor110analyzes the data representing the set of one or more subsequent video frames and determines (413) whether that video frame data includes data representative of the tracked pattern or patterns (e.g., determines whether any portion of the video frame data in the tracked video frames is substantially similar to the tracked pattern or patterns). If a tracked pattern is found in the data representing the set of subsequent video frames, the video processor110proceeds to determine whether the one or more tracked patterns are positioned suspiciously relative to a position of the person under surveillance. To make a suspicious positioning determination according to this particular exemplary embodiment, the video processor110determines (415) a distance between the tracked pattern and the person under surveillance. If a tracked pattern is not found in the data representing the set of subsequent video frames, the video processor110extracts (403) the next video frame from the video data and the process repeats.

To determine the distance between a tracked pattern and the person under surveillance, the video processor110may be programmed to measure pixel distances between points on the tracked pattern and points on the person for video captured from one or more statically-positioned cameras (e.g., cameras103,104). In other words, the video processor110may analyze the video frames in the video data streams received from one or more statically-positioned cameras capturing images of video capture areas that include the subject of the tracked pattern and the person under surveillance. The video processor110may also use the body camera of the person under surveillance (e.g., camera101) to aid in the determination of distance, such as by using video data from the body camera to determine an angle at which the subject of the tracked pattern is located relative to a reference axis. The video processor110may further determine the distance between the tracked pattern and the person under surveillance as a function of camera lens profile specifications for the camera from which the video data under analysis was received, a position of the tracked pattern within the video frame, and a size of the tracked pattern bounding area. For example, the video processor110may receive video data streams from two statically-positioned cameras to improve the accuracy of the potential threat assessment made by just using video data from the body camera of the person under surveillance. In another example, two or more statically-positioned cameras and the body camera of the person under surveillance may be used to generate a three-dimensional (3D) model of the person's environment and determine a distance vector between the tracked pattern and the person under surveillance.

Alternatively, the video processor110may be programmed to determine a distance between a tracked pattern and the person under surveillance by determining coordinates of the tracked pattern within a 3D environment model (Xi, Yi, Zi) generated from video data supplied by two or more statically-positioned cameras and the body camera of the person under surveillance, and computing the distance as follows:
Distance=SQRT[(Xi+n−Xi)2+(Yi+n−Yi)2+(Zi+n−Zi)2],where “i” is the frame index and “n” is the number of frames used to compute the distance.

In addition to determining a distance between each tracked pattern and the person under surveillance, the video processor110determines (417) a motion vector for each tracked pattern relative to the person under surveillance. To determine such a vector, the video processor110may be programmed to compute a velocity vector as follows:
Velocity Vector=[(Xi+n−Xi),(Yi+n−Yi),(Zi+n−Zi)]/(Ti+n−Ti)where “i” is the frame index, “n” is the number of frames used to compute the velocity vector, and “Ti” is the time corresponding to index i.

After the distance between the tracked pattern and the person under surveillance and the tracked pattern's motion vector have been determined, the video processor110determines (419) whether the determined distance is less than a threshold and whether the motion vector is in a general direction of the person under surveillance. When both conditions have been met according to this embodiment, the video processor alerts (421) the person under surveillance as to a potential threat. By contrast, when both conditions have not been met, the logic flow ends with respect to the currently processed set of video frames and may be restarted with respect to the next set of video frames.

For example, where the video processing system100is utilized to monitor potential threats to employees of a cash transport service, the threshold distance may be set to about thirty feet (about ten meters) and the motion vector may be deemed to be in the general direction of the employee when the motion vector is within a 40° range (+/−20°) about a longitudinal or optical axis of the employee's body camera. Thus, according to this example, the video processor110may determine that a tracked pattern is a potential threat if, in an analyzed video frame, the pattern is positioned less than thirty feet from the employee and is moving within a range of +/−20° from the longitudinal axis of the employee's body camera. When the distance and motion conditions have been met, the video processor110may alert the person under surveillance as to a potential threat. Such alerting may be achieved by, for example: activating a local alert (such as an audible and/or visual alarm); communicating, via the communication interface108, an alert message to a mobile application executing on a wireless communication device carried by the person; and/or communicating, via the communication interface108, at least some of the video data from the analyzed video stream (e.g., the last ten seconds or 300 video frames) to a mobile video processing and display application executing on a wireless communication device carried by the person. In the latter case, the application may be configured to automatically play and display the received video to enable the person under surveillance to promptly assess the potential threat and react thereto as necessary.

FIGS. 5-7illustrate an exemplary use case for the processes and system ofFIGS. 1-4. According to this exemplary scenario, a cash transport service employee512has driven into and parked in the automated teller machine (ATM) drive-thru lane of a bank. The vehicle522used to transport the employee512may be parked a few feet in front of the ATM514to be serviced, as generally shown inFIG. 5. In this particular situation, the video processing system may include a video processing apparatus and one or more cameras. Where the video processing system is a closed system, the cameras may include a camera501secured to the body of the employee512(e.g., as installed in or attached to a vest, jacket, shoulder harness or other item worn by the employee512while performing his job function) and one or more vehicle-mounted cameras502(one shown for illustration purposes). Where the video processing system is an open system capable of receiving and processing video data from third party video cameras, the cameras may further include a variety of cameras that may be positioned at or near the monitored location. Such cameras may include bank video surveillance cameras503-506, an internal ATM camera507, and video surveillance cameras508-510mounted outside nearby stores (e.g., of a nearby strip mall).

The video processing apparatus in the exemplary scenario ofFIG. 5may include a video processor516and a communication interface. The communication interface may include a short-range wireless interface, such as a Wi-Fi interface518, and/or a wide-area wireless interface, such as a 4G LTE interface520. The Wi-Fi interface518may be used to communicate video data and control signaling between the video processor516and the cameras501-510used in the particular implementation of the system, as well as between the video processor516and a wireless communication device530(e.g., smartphone) carried by the employee512(where such device530is used to provide threat alerts and/or related video to the employee512). The LTE interface520may be similarly used to communicate video data and control signaling between the video processor516and the body-mounted camera501, the vehicle-mounted camera502, and/or a wireless communication device530, but may be further used to communicate video data and other information between the video processor516(and/or the cameras501,502) and one or more remote devices, such as a remote control center for the cash transport service company, a law enforcement emergency response center, a cloud storage service, and/or any other remote device that may interface with the video processing system.

The video processing system may further include or be connected to a local alerting mechanism, such as a speaker521. The alerting mechanism may be controlled by the video processor516to alert (e.g., audibly alert in the case of speaker521) the employee512of a potential threat. In the embodiment illustrated inFIG. 5, the video processing apparatus is located entirely within the employee's transport vehicle522. In an alternative embodiment, the video processing apparatus and/or its function may be distributed, such that some or all of the video processor function is performed by one of more server instances in a cloud server. An exemplary architecture for a cloud-based implementation of the video processor110,516is described below with respect toFIG. 8.

For the sake of brevity and ease of understanding, operation of the video processing system in connection with the exemplary scenario illustrated inFIG. 5will be limited to considering video images captured by the employee's body-mounted camera501and the vehicle-mounted camera502. However, those of ordinary skill in the art will readily recognize and appreciate that the general principles of operation described below and otherwise herein may be applied to systems in which video and/or still images captured by other cameras503-510are considered in the threat determination process.

As shown in an exemplary manner inFIG. 5by dashed conically-shaped patterns, the body-mounted camera501captures images in a first video capture area524and the vehicle-mounted camera502captures images in a second video capture area525. Each video capture area524,525is defined by the particular characteristics of its respective camera501,502. As shown inFIG. 5, each video capture area524,525includes an area that is proximate the employee512, who is the person under surveillance in this example. The video capture area525of the vehicle-mounted camera502includes the employee512; whereas, the video capture area524of the body-mounted camera501is basically from the employee's viewing perspective in the direction and field of view of the camera501. Although depicted as a rearward-facing camera, the body camera501may alternatively be forward-facing and/or the employee512may wear multiple cameras facing in multiple directions.

In the exemplary scenario depicted inFIG. 5, two potential threats to the employee512are shown for illustrative purposes. The first potential threat is a person527who is walking in the general direction illustrated by the dashed arrow originating from the person527. The second potential threat is a parked car528positioned generally near the location of the employee512.

After the video processing system has been activated, each camera501,502begins capturing images from its respective video capture area524,525and communicating video data representing time-sequenced video frames to the video processor516. The video data may include metadata, such as time stamps (e.g., where each video camera501,502includes a global positioning satellite (GPS) unit or other accurate time source), or other information based upon which the video frames from each camera501,502can be time-synchronized. The video processor516receives the video data from the cameras501,502in real time or near real time using a streaming control protocol, such as RTSP, to control the streams of video data from the two cameras501,502. The video processor516analyzes the video data in each video frame from each camera501,502to determine whether the video frame data includes data representative of one or more potential threat patterns. The set of potential threat patterns may be stored in memory of, or otherwise accessible to, the video processor516. To determine whether a video frame received from a camera501,502includes a potential threat pattern, the video processor516may compare the video frame data to the previously stored data representative of the set of potential threat patterns. The set of potential threat patterns may include, for example, the outline or other features of a human body or a portion thereof, the outline or other features of one or more predetermined objects (such as a firearm, knife, bat, club, TASER, or other object that could be used as a weapon), and/or the outline or other features of a vehicle. The video processor516may be programmed to update and/or expand the stored potential threat pattern data by applying machine learning techniques, such as supervised learning techniques (e.g., classification and/or regression algorithms), unsupervised learning techniques (e.g., association, clustering, and/or dimensionality reduction algorithms), and/or reinforcement learning techniques, to video data received by the video processor516from the system's cameras501,502over time.

When the video processor516has determined that at least a portion of the video frame data includes data substantially similar to stored data representative of one or more potential threat patterns, the video processor516may determine that the video frame data includes potential threat pattern data. As discussed above with respect toFIG. 2, the video processor516may determine video data is substantially similar to potential threat pattern data where the video data has at least a fifty percent (50%) correspondence with data for a particular potential threat pattern within the set of potential threat patterns. In an alternative embodiment, the video processor516may determine whether the video frame data includes potential threat pattern data or other predefined pattern data by comparing combinations of position and velocity vectors for multiple simultaneously-tracked patterns to prestored reference combinations of position and velocity vectors and assigning a threat probability for each tracked pattern based on the degree of correspondence between the combination of position and velocity vector for the tracked pattern and one or more prestored reference combinations of position and velocity vectors.

When the video processor516has determined that at least a portion of the video frame data includes data representative of one or more potential threat patterns, the video processor516commences tracking of such pattern or patterns within the video data received from the cameras501,502. Pattern tracking may be performed on a video frame-by-video frame basis or on any other periodic or aperiodic basis (e.g., every other video frame, every fifth video frame, every third video frame during daylight hours, but every video frame during nighttime hours, and so forth). According to one exemplary embodiment, the video processor516may define a bounding area for each tracked pattern and initiate tracking to monitor for changes to the tracked pattern bounding area over time, especially within each camera's video capture area. For example, once a tracked pattern is detected in video data representing a video frame, the video processor516may position a shape as a boundary around the tracked pattern to form a trackable area for purposes of reducing the amount of processing resources necessary to track the pattern and its positioning relative to the employee512. In other words, when a particular predefined pattern has been detected within a video frame, the pattern may be “bounded” within a reference area to make evaluating the pattern's positioning over multiple video frames and the potential threat to the employee512less processing intensive.

Pattern tracking may be commenced immediately upon detecting that video frame data includes data representative of one or more potential threat patterns or pattern tracking may be commenced selectively, such as only when certain other conditions are met. For example, the video processor516may use characteristics of the bounding area as a basis for deciding whether or not to initiate and perform pattern tracking. In such a case, the bounding area characteristics based upon which the video processor516may decide to initiate and perform pattern tracking include the size of the bounding area, the proximity of one or more points within the bounding area or on one or more of its edges to a location of the employee512, and/or the presence of one or more other potential threat patterns in or near the bounding area. For example, the video processor516may determine a location of the tracked pattern bounding area (e.g., within or along an edge of the tracked pattern bounding area) relative to a location of the employee512and selectively initiate pattern tracking only when the location of the tracked pattern bounding area is estimated to be within a threshold distance (e.g., within about 45 feet or 14 meters) of the location of the employee512. As another example, the video processor516may determine bounding areas of multiple tracked patterns (e.g., tracked patterns for a vehicle528and one or more persons527) within the video frame data of the cameras501,502and selectively initiate pattern tracking only when the location of the tracked pattern bounding areas for two or more of the tracked patterns are estimated to be within a threshold distance (e.g., about 15 feet or 5 meters) of one another.

After pattern tracking has been commenced, the video processor516determines whether data representing one or more subsequent video frames includes data representative of the tracked pattern or patterns. In other words, after pattern tracking has commenced, the video processor516analyzes some or all of the data representing video frames subsequent in time to the video frame that triggered the tracking to determine whether such data includes any tracked pattern or patterns. Such analysis may include comparing some or all of the video data representative of a subsequent video frame to previously stored data representative of one or more stored potential threat patterns or comparing some or all of the video data representative of a subsequent video frame to data representative of a potential threat pattern detected in a prior video frame. According to one exemplary embodiment, the video processor516analyzes video frame data on a periodic basis after pattern tracking has commenced. For example, the video processor516may analyze data representing ten consecutive video frames where the camera501,502supplying the video data is capturing images at a rate of thirty frames per second (30 fps). In such a case, the video processor516analyzes every 333 milliseconds of video data to determine whether such data includes the tracked pattern(s) after pattern tracking has commenced. As another example, the video processing system may analyze data representing fifteen consecutive video frames where the camera501,502supplying the video data is capturing images at a rate of sixty frames per second (60 fps). In this particular case, the video processor516may analyze every 250 milliseconds of video data to determine whether such data includes the tracked pattern(s) after pattern tracking has been commenced. The quantity of video frames analyzed by the video processing system may be selected based on several factors, including camera video quality, location and/or size of video capture area, positioning of the person within the video capture area, quantity and type of physical and natural structures in or near the video capture area, and so forth.

When data representing one or more subsequent video frames includes data representative of the tracked pattern or patterns, the video processor516determines whether the tracked pattern or patterns are positioned suspiciously relative to the employee512. According to one exemplary embodiment, the video processor516may determine whether the analyzed data includes data indicative of movement of the tracked pattern or patterns (or their respective bounding areas) in a potentially threatening manner relative to the employee512. For example, the video processor516may compare the size and positioning one or more tracked patterns in one subsequent video frame to data representative of the same tracked pattern or patterns in one or more other subsequent video frames. According to one embodiment, the video processor516may set the position of the employee512as a reference origin for images captured by either or both cameras501,502. The video processor516may then determine whether the tracked pattern bounding area is becoming larger and/or closer to a bottom of each image in the analyzed subsequent video frames based upon the data representing the subsequent video frames. When the tracked pattern bounding area is becoming larger and/or closer to a bottom of each image in the subsequent video frames, the video processor may determine that the tracked pattern is positioned suspiciously relative to the position of the employee512or other person under surveillance.

FIG. 6provides an illustration for how the video processor516may analyze a set of video frames to initiate and continue pattern tracking. According to this embodiment, the video processor516receives streaming video data from a camera (e.g., camera501) and extracts therefrom data representing a video frame601(e.g., Video Frame N inFIG. 6). The video processor516compares the video frame data to data representing a set of potential threat patterns. In the illustrated case, the set of potential threat patterns includes one or more patterns for a person527and the video processor516determines that the outline of a person527is substantially similar to a stored potential threat pattern614. In response to such determination, the video processor516defines a bounding area606for the detected pattern614by overlaying the pattern614with a simpler geometric shape (e.g., a rectangle in this particular case).

According to one exemplary embodiment, the video processor516may commence pattern tracking upon defining the tracked pattern bounding area606. According to another exemplary embodiment, the video processor516may determine a location of the tracked pattern bounding area606relative to a location of the employee512and then initiate pattern tracking when the location of the tracked pattern bounding area606is estimated to be within a threshold distance of the location of the employee512. To determine the distance between the tracked pattern bounding area606and the employee512, the video processor516may set the position of the employee512or other person under surveillance as the reference origin for the images captured by the camera501and determine a pixel or other distance612between a point or pixel coordinate608on an edge (e.g., bottom edge) of the bounding area606and a corresponding point or coordinate610along an edge (e.g., bottom edge) of the video frame601. When the determined distance612is less than a predefined threshold distance (e.g., a pixel distance that equates to an actual, physical distance of less than about 100 feet or about 30 meters, or such other distance as may be defined by the system operator), the video processor516may commence pattern tracking.

According to the embodiment illustrated inFIG. 6, the video processor516may set the position of the employee512or other person under surveillance as the reference origin for images captured by the camera501, if the video processor516hasn't already done so when determining whether to commence pattern tracking. Setting the position of the employee512or other person under surveillance as the reference origin provides a point of view for the video processor516to assess the potential threat of the tracked pattern's subject to the employee512. To evaluate the potential threat, the video processor516may monitor the size of the tracked pattern bounding area606over a set of video frames602-604that are subsequent in time to the video frame601that resulted in commencement of pattern tracking (three video frames602-604are shown in the set of subsequent video frames for illustration, but the set may include ten or more video frames as described above). The set of subsequent video frames602-604over which a tracked pattern is analyzed may be sequential in nature (e.g., using the nomenclature fromFIG. 6, Mymay equal Mx+1and Mzmay equal My+1) or may be otherwise selected over the tracking time period (e.g., Mymay equal Mx+2, Mzmay equal My+3, and so forth based on how the frames to be analyzed are selected).

When the size of the tracked pattern bounding area606becomes larger over the set of subsequent video frames602-604(e.g., as illustrated inFIG. 6), the video processor516may determine that the tracked pattern614is approaching the employee512and, therefore, is positioned suspiciously relative to the employee512. To determine whether the tracked pattern bounding area606is becoming larger over several video frames, the video processor516may use statistical processing to analyze the measured bounding area sizes. For example, the video processor516may determine a linear regression from the bounding area size data to represent how the size of the tracked pattern bounding area606changes across the set of subsequent video frames602-604. The video processor516may then determine a gradient for the linear regression and compare the gradient to a threshold. For example, in the context of a potentially threatening person approaching the employee512, the gradient threshold may be set in the range of 0.040 and 0.060, which equates to a 4.0% to 6.0% increase in boundary area size per second. When the gradient is greater than its threshold, the video processor516determines that the tracked pattern bounding area606is becoming larger over the set of subsequent video frames602-604.

Additionally or alternatively, the video processor516may be programmed to determine whether the tracked pattern bounding area606is becoming closer to a bottom of each image in the subsequent set of video frames602-604. Where the position of the employee512or other person under surveillance is set as the reference origin for images captured by the camera501, movement of the tracked pattern614toward the bottom of the image over multiple video frames indicates that the tracked pattern614is approaching the person under surveillance (e.g., employee512) and, therefore, may be a potential threat to the person under surveillance. According to this embodiment, the video processor516determines a position of a coordinate608along a bottom edge of the tracked pattern bounding area606and a relationship between the position of the coordinate608along the bottom edge of the tracked pattern bounding area606and the reference origin for each video frame601-604being analyzed. In the example illustrated inFIG. 6, the relationship between the position of the coordinate608along the bottom edge of the tracked pattern bounding area606and the reference origin is a distance612(e.g., pixel distance) between the coordinate608along the bottom edge of the tracked pattern bounding area606and a coordinate610along the bottom edge of the image as defined by the dimensions of the video frame601-604. For illustration purposes only, the coordinate608along the bottom edge of the tracked pattern bounding area606is approximately centered along the bottom edge of the tracked pattern bounding area606and the coordinate610along the bottom edge of the image is likewise centered along the bottom edge of the image.

To determine whether the tracked pattern bounding area606is becoming closer to the bottom of the image over the analyzed subsequent video frames602-604, the video processor516may use statistical processing to analyze the change in relationship (e.g., distance) between the tracked pattern bounding area606and the bottom of each image. For example, the video processor516may determine a linear regression from the bounding area-to-reference image distance data to represent how the relationship between the position of the coordinate608along the bottom edge of the tracked pattern bounding area606and the reference origin changes across the set of subsequent video frames602-604. The video processor516may then determine a gradient for the linear regression and compare the gradient to a threshold. For example, in the context of a potentially threatening person approaching the employee512, the gradient threshold may be set in the range of −0.010 and −0.020, which equates to a 1% to 2% decrease in distance per second. When the gradient is less than its threshold, the video processor516determines that the tracked pattern bounding area606is becoming closer to the bottom of each image (and, therefore, closer to the reference origin) over the set of subsequent video frames602-604. The video processor110,516may analyze bounding area size changes, bounding area positioning relative to a reference origin or other reference point, both bounding area size changes and bounding area positioning, and/or any other video data-based characteristics to make its final determination as to whether a tracked pattern is positioned suspiciously relative to a position of the person under surveillance.

According to another exemplary embodiment, the video processor516may compare data representative of a tracked pattern614in one set of subsequent video frames602,603to data representative of the tracked pattern614in another, later-in-time set of subsequent video frames603,604. Responsive to such comparison, the video processor516may determine one or more motion vectors that represent movement of the tracked pattern614over time. Thereafter, the video processor516may determine, based on the motion vector or vectors, whether the tracked pattern614is moving generally toward the person under surveillance (e.g., employee512). When the tracked pattern614is moving generally toward the employee512, the video processor516may determine a distance between the tracked pattern614and the employee512. When the determined distance is less than a threshold, the video processor516may determine that video data representing the one or more subsequent video frames602-604includes data indicative of movement of the tracked pattern614in a potentially threatening manner relative to the employee512. To assess whether the tracked pattern614is moving generally toward the employee512, the video processor516may determine whether the tracked pattern614is moving directly toward the employee512or toward a position that is close enough to the employee512to pose a threat to the employee512depending on, for example, the details of the tracked pattern614, or is moving on a path that will, with a high probability, intersect with or be in close proximity to a path of the employee512.

According to another exemplary embodiment, the video processor516may receive motion data associated with the employee512or other person under surveillance, where the motion data is time-synchronized with the video data. For example, the motion data may be received from the employee's body camera501, such as from one or more motion sensors (e.g., accelerometer, gyroscope, global positioning system (GPS), or other sensors) embedded within the camera501, or from a mobile device530carried by the employee512(e.g., from a smartphone running a mobile application that is time-synchronized with the employee's body camera501). Where the motion data is supplied by the employee's body camera501, the motion data may be received by the video processor516as metadata within the video data stream from the camera501.

Where motion data for the employee512or other person under surveillance is received in addition to video data, the video processor516may use the motion data to assist with determining whether one or more tracked patterns are positioned suspiciously relative to the employee512or other person under surveillance. In such a case, when the video processor516determines that the employee512is in motion, the video processor516may further determine, based on video data over multiple video frames, whether the tracked pattern614is becoming substantially smaller in size (e.g., at least twenty-five percent smaller over one or more video frames) or is no longer present in the video capture area524. When the employee512is in motion and the tracked pattern614is not becoming substantially smaller in size and/or remains present in the video capture area524, the video processor516may determine that the tracked pattern614is positioned suspiciously relative to the position of the employee512. For example, not having the tracked pattern614become substantially smaller and/or having the tracked pattern614remain in the video capture area524could indicate that the person527represented by the tracked pattern614is following the employee512or other person under surveillance. Alternatively, when the employee512is in motion and the tracked pattern614is becoming substantially smaller in size or is no longer present in the video capture area524, the video processor516may determine that the tracked pattern614is not positioned suspiciously relative to the position of the employee512. According to one exemplary embodiment, the video processor516may be programmed to consider a decrease in the size of the tracked pattern614or the tracked pattern's bounding area606by at least twenty-five percent over the analyzed video frames601-604to indicate that the tracked pattern614is becoming substantially smaller in size for purposes of assessing whether the tracked pattern614is positioned suspiciously relative to the position of the employee512.

FIG. 7provides an illustration for how the video processor516may analyze a set of video frames701-704in connection with receipt of motion data associated with a person under surveillance (e.g., employee512). According to this embodiment, the video processor516receives streaming video data from a camera (e.g., camera501) and extracts therefrom data representing a video frame701(Video Frame N). The video data stream or metadata thereof may include motion data representing outputs from one or more motion sensors within the camera501. For example, the motion data may have been inserted by the camera501into the video data stream through use of supplemental enhancement information (SEI) messages in accordance with the H.264 video codec (MPEG-4 Advanced Video Coding Part10) standard. As detailed above with respect toFIG. 6, the video processor516compares the video frame data to stored data representing a set of potential threat patterns. In the illustrated case, the set of potential threat patterns includes one or more patterns for a person527and the video processor516determines that the outline of a person527is substantially similar to a stored potential threat pattern714. In response to such determination, the video processor516defines a bounding area706for the detected pattern714by overlaying the pattern714with a simpler geometric shape (e.g., a rectangle in this particular case).

According to one exemplary embodiment, the video processor516may commence pattern tracking upon defining the tracked pattern bounding area706. According to another exemplary embodiment, the video processor516may determine a location of the tracked pattern bounding area706relative to a location of the person under surveillance and then initiate pattern tracking when the location of the tracked pattern bounding area706is estimated to be within a threshold distance of the location of the person under surveillance. To determine the distance between the tracked pattern bounding area706and the person under surveillance, the video processor516may set the position of the person under surveillance as the reference origin for the images captured by the camera501and determine a pixel or other distance712between a point or pixel coordinate708on an edge (e.g., bottom edge) of the bounding area706and a corresponding point or coordinate710along an edge (e.g., bottom edge) of the image or video frame701. When the determined distance712is less than a predefined threshold distance, the video processor516may commence pattern tracking.

According to the embodiment illustrated inFIG. 7, the video processor516may set the position of the person under surveillance as the reference origin for images captured by the camera supplying the video data (e.g., body camera501), if the video processor516hasn't already done so when determining whether to commence pattern tracking. To evaluate a potential threat, the video processor516may monitor the size of the tracked pattern bounding area706over a set of video frames702-704that are subsequent in time to the video frame701that resulted in commencement of pattern tracking (three video frames702-704are shown in the set of subsequent video frames for illustration, but the set may include ten or more video frames as described above). The set of subsequent video frames702-704over which a tracked pattern is analyzed may be sequential in nature (e.g., using the nomenclature fromFIG. 7, Mymay equal Mx+1and Mzmay equal My+1) or may be otherwise selected over the tracking time period (e.g., Mymay equal Mx+2, Mzmay equal My+3, and so forth based on how the frames to be analyzed are selected).

When the video processor516determines from the motion data that the person under surveillance is in motion (e.g., walking) and further determines from analyzing the data representing the set of subsequent video frames702-704that the size of the tracked pattern bounding area706is becoming substantially smaller in size or that the tracked pattern714is no longer present in the video captured from the camera's video capture area524, the video processor516may determine that the tracked pattern714is not positioned suspiciously relative to the person under surveillance. On the other hand, when the video processor516determines from the motion data that the person under surveillance is in motion and further determines from analyzing the data representing the set of subsequent video frames702-704that the size of the tracked pattern bounding area706is not becoming substantially smaller in size and that the tracked pattern714remains present in the video captured from the camera's video capture area524, the video processor516may determine that the tracked pattern714is positioned suspiciously relative to the person under surveillance.

In an alternative embodiment, the video processor516may analyze the distance712between the tracked pattern714or its associated bounding area706and a bottom of the video frame image across the analyzed set of video frames701-704. To determine the distance between the tracked pattern bounding area706and the person under surveillance (e.g., employee512), the video processor516may set the position of the person under surveillance as the reference origin for the images captured by the camera501and determine a pixel or other distance712between a point or pixel coordinate708on an edge (e.g., bottom edge) of the bounding area706and a corresponding point or coordinate710along an edge (e.g., bottom edge) of the image or video frame701. When the video processor516determines from the motion data that the person under surveillance is in motion and further determines from analyzing the data representing the set of subsequent video frames702-704that the distance712between the bottom edge coordinate708of the tracked pattern bounding area706and the bottom edge coordinate710of the video frame702-704is increasing, the video processor516may determine that the tracked pattern714is not positioned suspiciously relative to the person under surveillance. On the other hand, when the video processor516determines from the motion data that the person under surveillance is in motion and further determines from analyzing the data representing the set of subsequent video frames702-704that the distance712between the bottom edge coordinate708of the tracked pattern bounding area706and the bottom edge coordinate710of the video frame702-704is decreasing or remaining substantially unchanged, the video processor516may determine that the tracked pattern714is positioned suspiciously relative to the person under surveillance. As described above with respect toFIG. 6, the change in distance712from the bounding area edge to the frame/image edge may be used alone or together with the change in the size of the bounding area706to determine whether the tracked pattern714is positioned suspiciously relative to the person under surveillance when the person under surveillance is in motion.

The exemplary set of video frames701-704depicted inFIG. 7show one example where the size of the bounding area706remains substantially unchanged over the analyzed set of video frames701-704. As a result, where the motion data associated with the person under surveillance indicates that the person under surveillance is in motion, the video data in combination with the motion data indicate to the video processor516that the person527represented by the tracked pattern714may be following the person under surveillance and that the tracked pattern714is, therefore, positioned suspiciously relative to the person under surveillance.

The exemplary set of video frames701-704depicted inFIG. 7also show one example where the distance712between the bottom edge coordinate708of the tracked pattern bounding area706and the bottom edge coordinate710of the video frame702-704remains substantially unchanged. As a result, where the motion data associated with the person under surveillance indicates that the person under surveillance is in motion, the video data in combination with the motion data indicate to the video processor516that the person527represented by the tracked pattern714may be following the person under surveillance and that the tracked pattern714is, therefore, positioned suspiciously relative to the person under surveillance.

After one or more tracked patterns614,714have been determined to be positioned suspiciously relative to the position of the person under surveillance (e.g., employee512), the video processor516may alert the person under surveillance of a potential threat. For example, the video processor516may communicate a message to an application executing on the employee's wireless communication device530, where the message causes the application to activate an audible alarm and/or a haptic alarm of the wireless communication device530. Alternatively, the video processor516may communicate at least some of the video data to a video processing and display application executing on the employee's wireless communication device530. Such video data may include static images, a video stream, or both to enable the employee512to independently analyze any potential threat. Alternatively, when a tracked pattern bounding area606,706is defined for a tracked pattern614,714, the video data communicated to the employee's wireless device530may be augmented with data representing at least one overlay for the tracked pattern bounding area606,706. For example, when a rectangular bounding area606,706is defined for the tracked pattern614,714, the video data communicated to the employee's wireless device530may be augmented with data representing a rectangle overlay positioned over the tracked pattern614,714so as to visibly indicate the tracked pattern bounding area606,706to the employee512.

FIG. 8is a block diagram illustrating a cloud-based architecture800for implementing a threat detection method based on real-time or near real-time video analysis, in accordance with a further exemplary embodiment of the present disclosure. The exemplary cloud architecture800may include or utilize multiple cloud server instances, including, for example, a processing instance801, an analyzing instance802, and a distribution instance803. The processing instance801includes software modules that operate to, inter alia, receive (805) streaming video from the video sources (e.g., cameras), transrate and/or transcode (807) the video frames of the video stream, and optionally perform frame synchronization (809) by, for example, determining frame timing from the received video data and supplying frame synchronization signals to various functions within the analyzing instance802and the distribution instance803. The frame synchronization function (809) may be necessary for video streams, such as MJPEG streams, that do not provide timing themselves. The frame synchronization function (809) is unnecessary for video streams, such as MPEG-4 and H.264 streams, that include video frame presentation time information in their respective container or wrapper formats.

The analyzing instance802includes software modules that operate to, inter alia, analyze (811) the video frame data in real time or near real time to determine whether the video frame data includes one or more stored patterns and, if so, track the pattern or patterns over a set of subsequent video frames in the video stream. The analyzing instance802may also include software modules to create (813) metadata that may be individually accessible or that may be included with or accompany the video stream. Once created, metadata may be stored in a database together with the presentation time and the video stream identifier of the video frame and video stream to which the metadata respectively relates. At the time of distribution by the distribution instance803, the analyzing instance802may arrange (815) the created metadata into a frame structure that mirrors the frame structure of the video data stream to be forwarded to an end user. Frame synchronization for analyzing the video frame data may also be provided, when necessary, from the frame synchronization function (809) executing in the processing instance801.

The distribution instance803includes software modules that operate to, inter alia, forward (817) the originally-received video stream to a requesting client application, create (819) and communicate to the client application a metadata stream for use by the client application to augment the original video stream, or create (821) and communicate to the client application a combined video and metadata stream that already includes the tracked pattern bounding area overlaid upon the original video stream. Where the metadata is integrated into a combined video and metadata stream, the metadata may be inserted into the video stream as SEI messages when the video data stream is created according to the H.264 video codec. Frame synchronization for creating the metadata stream and/or the combined video and metadata stream may be provided, when necessary, from the frame synchronization function (809) executing in the processing instance801. The client application to which the video and/or metadata stream is sent may be, for example, a mobile application running on the monitored person's wireless device530, an enterprise or other software application running on a server/computer at a surveillance monitoring location, an Internet application (e.g., a media player), a web browser, or any other software program that permits viewing videos.

To implement the cloud-based architecture800ofFIG. 8according to one exemplary embodiment, a video streaming engine (such as the commercially-available WOWZA video streaming engine) and an object detection process (such as the commercially-available YOLO object detection system) may be run simultaneously on cloud server instances provided through a web services company, such as Amazon Web Services, Inc. (“AWS”). In such a case, the video streaming engine receives (805) one or more video streams from one or more cameras101-104,501-510over the Internet. To achieve low latency in furtherance of performing real-time or near real-time video processing, the cameras used in the video processing system may use the Real-Time Messaging Protocol (RTMP), which is an open specification from Adobe Systems Incorporated, to transmit their video streams to the cloud-based processing instance801. The video streaming engine transrates (807) each video stream and runs the object detection process on it. The object detection process analyzes (811) each video frame of the video stream and detects any pre-stored patterns in the video frame. Once a pattern is detected, the detected pattern may be tracked by running a threat detection algorithm over a set of subsequent video frames (e.g., a set of 10-20 consecutive video frames following or including the video frame in which the pattern was originally detected). Based on the results of the threat detection algorithm, metadata may be created (813) to facilitate placement of a geometrically-shaped overlay over the tracked pattern to form a tracked pattern bounding area. The metadata may contain the type of geometric shape, positioning of the shape in the video frame, a class name for the tracked pattern (e.g., person, car, weapon, etc.), and a probability that such pattern was accurately detected. The video streaming engine may then create (819,821) a metadata stream and/or a combined video and metadata stream (video stream augmented with the tracked pattern overlay) and provide (817,821,823) the original video stream, the metadata stream, and/or the combined video and metadata stream to one or more client applications via the Internet.

The cloud-based architecture800illustrated inFIG. 8or another similarly-configured architecture may be also or alternatively used to perform video post-processing of one or more videos previously recorded by one or more cameras101-104,501-510. In such a case, the recorded video files may be uploaded to a storage unit or bucket of a cloud storage service, such as the AWS S3 service. After uploading has been completed, a compute service, such as the AWS LAMBDA service, may be automatically or manually triggered to run a processing script on the processing instance801. The processing script downloads the video files (video data) from the cloud storage service into local storage of the cloud server. The video data may then be processed in the same manner as described above with respect to processing of streaming video to ultimately create overlay metadata associated with a video frame or a series of video frames in the processed video data. The created metadata may be stored in a separate file or new videos may be created based on the metadata and the original video data. When created, such new video files may be uploaded to the cloud storage service (e.g., into a new storage unit, such a new AWS S3 bucket) and the original video files may be deleted from the local storage of the cloud server. One exemplary reason to use cloud-based video post-processing may be to generate a highlight or summation video from videos captured by different cameras101-104,501-510so as to enable a pattern to be tracked from different viewing angles.

FIG. 9is a process flow diagram900of steps executed by a video processing system100to detect suspicious activity, including a potential threat, to a person based on real-time or near real-time analysis of video data supplied by one or more cameras in accordance with a further exemplary embodiment of the present disclosure. According to this embodiment, the video processing system100, through operation of its communication interface108and video processor110, receives (901) one or more video data streams from one or more respective cameras101-104. The cameras101-104may be mounted to or within one or more objects, such as a vehicle, a light pole, an awning or canopy, a wall, a roof, a structural support pole, a telephone pole, a tree, an automated teller machine (ATM), or any other object. The video processor110may also be programmed to use a streaming control protocol, such as RTSP, to control the video data streams from the cameras101-104when multiple cameras101-104are used.

As each video data stream is received at the video processor110, the video processor110extracts (903) data representing a set of one or more video frames from the video data based on the video streaming protocol and the video codec used by the respective camera101-104and the video processor110. Responsive to extracting the video frame data, the video processor110determines (905) whether the video frame data includes data representing (or equivalently, representative of) an image of the person under surveillance and data representing one or more predefined patterns. As discussed above with respect toFIGS. 1 and 4, the video processor110may compare portions of the video frame data to data representative of a set of predefined patterns previously stored in memory114to determine whether a video frame or any portion thereof includes data substantially similar to data representing a predefined pattern. The predefined patterns may include, inter alia, object patterns, animal patterns, and general human image patterns. The video processor110may further compare portions of the video frame data to data representative of a set of human image patterns previously stored in memory114to determine whether the video frame or any portion thereof includes data substantially similar to data representing an image of the person under surveillance. The process flow ofFIG. 9contemplates that the video processing system100may be used to provide suspicious activity alerts to multiple persons under surveillance either simultaneously or at different times. Thus, the system memory114may include one or more databases of human image patterns representing images of persons who may be subject to surveillance by the video processing system100over time.

When the video frame data from a particular camera101-104, or from multiple cameras101-104over a synchronized time period (e.g., a period of 500 video frames), does not include data representing one or more predefined patterns and data representing an image of the person under surveillance, the video processor110extracts (907) data representing the next set(s) of one or more video frames from the video data stream(s) and determines (905) whether that video frame data includes data representing an image of the person under surveillance and data representing one or more predefined patterns. When the video frame data from a particular camera or set of cameras includes data representing one or more predefined patterns and data representing an image of the person under surveillance, the video processor110commences independently tracking (909) the image of the person under surveillance and the detected pattern or patterns within the video data and extracts (911) data representing one or more later-in-time sets of video frames from the video data stream or streams. The video processor110analyzes the later-in-time video frame data to determine (913) whether such video frame data continues to include data representing the image of the person under surveillance. So long as analyzed video frame data continues to include data representing an image of the person under surveillance, the video processor110continues to independently track (909) the image of the person under surveillance and the detected pattern or patterns within the video data. The video processor110may also contemporaneously perform the processes described above with respect toFIGS. 2-7to alert the person under surveillance as to suspicious activity, including potential threats, while such independent person and pattern tracking continues.

Person and pattern tracking may be performed using bounding areas, such as those described above with respect toFIGS. 3 and 6. For example, a bounding area may be defined by the video processor110for each predefined pattern that is detected and for the person under surveillance. The bounding areas may then be monitored for changes over time to determine whether the person under surveillance has left the system's video capture area(s) and/or whether a tracked pattern is headed toward a prior position or an estimated current position of the person under surveillance. Additionally, the video processor110may determine a location of a tracked pattern bounding area relative to the estimated current position or a prior position of the person under surveillance and initiate monitoring for changes to the tracked pattern bounding area only if the location of the tracked pattern bounding area is estimated to be within a threshold distance of the estimated current position or the prior position of the person under surveillance. The process of defining bounding areas and using them for identification and tracking purposes substantially reduces the processing resources necessary to reliably track patterns and persons over large quantities of video frames.

When the later-in-time video frame data is determined (913) to exclude data representing an image of the person under surveillance, the video processor110continues (915) independently tracking data representing the previously detected pattern or patterns within video frame data representing further later-in-time sets of one or more video frames received from the one or more cameras101-104. In other words, according to the process embodiment depicted inFIG. 9, the video processor110continues tracking the tracked pattern or patterns in received video frame data after the person under surveillance has left the video capture area(s) of the video camera(s)101-104. If the video processor110determines (917) that a tracked pattern is positioned suspiciously relative to either a prior position of the person under surveillance within the video capture area(s) of the system's video camera(s)101-104or an estimated current position of the person under surveillance (e.g., a position at which the person under surveillance was last determined to be prior to leaving the video capture area(s) of the camera(s)101-104, or a position of the person as reported to the video processing system100via an out-of-system means, such as through use of a third party camera or report), then the video processor110alerts (919) the person under surveillance of a potential threat or other suspicious activity. If, on the other hand, the video processor110never determines (917) that a tracked pattern is positioned suspiciously relative to either a prior position of the person under surveillance within the video capture area(s) of the system's video camera(s)101-104or an estimated current position of the person under surveillance, the tracked pattern monitoring process ends.

To determine whether a tracked pattern is positioned suspiciously relative to a prior position or an estimated current position of the person under surveillance, the video processor110may employ the techniques described above with respect toFIGS. 2-6. However, when using such techniques, the position of the person under surveillance would be replaced by either a prior position of the person under surveillance (e.g., as determined by the video processor110from positions occupied by the person under surveillance when the person was within the video capture area(s) of the system's camera(s)101-104) or an estimated current position of the person under surveillance (e.g., a position at which the person under surveillance was last determined to be prior to leaving the video capture area(s) of the camera(s)101-104, or a position of the person as reported to the video processing system100via an out-of-system means, such as through use of a third party camera or report). For example, the video processor110may determine whether video frame data, as extracted from received video data, includes data indicative of movement of one or more tracked patterns in a potentially threatening manner relative to the person under surveillance. For instance, the video processor110may compare data representing one or more tracked patterns in one set of video frames to data representing the same tracked pattern(s) in at least one subsequent or other later-in-time set of video frames to determine a motion vector (e.g., velocity) for each such tracked pattern representing movement of the tracked pattern over time. Responsive to determining the motion vector(s), the video processor110may determine whether each motion vector is in a general direction of either a prior position of the person under surveillance or an estimated current position of the person under surveillance. In other words, the video processor110uses the motion vector for a tracked pattern to determine whether the tracked pattern is moving generally toward a prior position or an estimated current position of the person under surveillance.

When the one or more motion vectors are determined to be in a general direction of a prior position or an estimated current position of the person under surveillance, the video processor110may determine that the video frame data includes data indicative of movement of one or more tracked patterns in a potentially threatening manner relative to the person under surveillance. For example, the video processor110may determine whether the motion vector indicates that a tracked pattern is on a track to intercept or pass near a prior position or an estimated current position of the person under surveillance. In such a case, if a tracked pattern is projected to intercept or pass near a prior position or an estimated current position of the person under surveillance within a threshold time period in the future (e.g., within five seconds or 150 video frames), the video processor110may determine that the tracked pattern is positioned suspiciously relative to the person under surveillance. Alternatively, when the one or more motion vectors are determined to be in a general direction of a prior position or the estimated current position of the person under surveillance, the video processor110may estimate, based upon the one or more motion vectors, one or more distances between the one or more tracked patterns and the estimated current position or a prior position of the person. In this case, when a distance between a tracked pattern and the estimated current position or a prior position of the person is less than a threshold (e.g., fifty feet), the video processor110may determine that the tracked pattern is positioned suspiciously relative to the estimated current position or a prior position of the person, and proceed to alert the person.

According to one exemplary embodiment, tracking of predefined patterns further continues if and when the person under surveillance returns into the video capture area(s) of the system's video camera(s)101-104until surveillance is no longer necessary (e.g., the messenger, security guard, or other person under surveillance returns to his or her vehicle and leaves the scene). In other words, the processes described above with respect toFIGS. 2-7continue to be performed when the person under surveillance returns into the video capture area(s) of the system's video camera(s)101-104so as to determine whether any threat may be posed to the person.

The video processor110may alert (919) the person under surveillance using one or more of a variety of methods, including those described above with respect toFIGS. 2-7. For example, the video processor110may activate a local alert, such as activate an audible and/or visual alarm or send an audio message to a local sound speaker, to notify the person. Alternatively, the video processor110may communicate, via the communication interface108, an alert message to a mobile application or another application (e.g., operating system application) executing on a wireless communication device carried by the person under surveillance (e.g., smartphone, cellular phone, tablet computer, personal digital assistant). In the latter case, the alert message may cause the application to activate an audible alarm and/or a haptic alarm of the wireless communication device and display textual, graphical, and/or other information to notify the person of the suspicious activity. Further, the video processor110may generate a report containing information regarding the one or more tracked patterns and communicate the report, via the communication interface108, to the application executing on the wireless communication device carried by the person under surveillance. The report may include details regarding the suspicious activity and/or a threat assessment as determined and inserted by the video processor110, or another locally or remotely connected processor, based on data representing video frames that include the predefined pattern or patterns. The threat assessment may be a number on a scale (e.g., a scale of one to five), a color code (e.g., red, yellow, green), or any other mechanism for generally or specifically quantifying a threat level associated with the detected suspicious activity, if any.

In the event that the wireless communication device carried by the person under surveillance had previously lost communication contact with the video processing system100(e.g., because the communication device left the coverage area of the video processing system's Wi-Fi network), the video processor110may delay communicating the alert (including any suspicious activity report) to the wireless communication device until after the wireless communication device regains communication contact with the video processing system100. Alternatively or additionally, the video processor110may alert the person under surveillance of detected suspicious activity before the person returns to the video capture area(s) of the video processing system100(i.e., before an image of the person under surveillance reappears in data representing a future set of one or more video frames received from the one or more video cameras101-104) so long as the wireless communication device carried by the person under surveillance continues to remain in communication contact with the video processing system100.

Still further, the video processor110may communicate, via the communication interface108, at least some of the video data from the analyzed video stream(s) (e.g., the last ten seconds or 300 video frames) to a video processing and display application executing on the wireless communication device carried by the person under surveillance. In this case, the application may be configured to automatically play and display the received video to enable the person under surveillance to assess the suspicious activity and react thereto as necessary. According to an alternative embodiment, the video processor110may select sequences of video frames from received video frames to create one or more video clips that include the one or more tracked patterns and insert the video clips into a suspicious activity report communicated to the person under surveillance's wireless communication device. The inserted video clips may then be played by an application installed on or accessible from the person's wireless device. As noted above, such a report may further include details regarding the suspicious activity and/or a threat assessment.

FIG. 10is a process flow diagram1000of steps executed by a video processing system100to detect suspicious activity, including a potential threat, to a person based on real-time or near real-time analysis of video data supplied by one or more cameras in accordance with a further exemplary embodiment of the present disclosure. The process flow depicted inFIG. 10is similar to the process flow described above with respect toFIG. 9, except that instead of independently tracking one or more predefined patterns and an image of the person under surveillance after detecting data representing both in video frame data received from one or more cameras101-104of the video processing system100, the video processor110tracks one or more predefined patterns only after initially detecting an image of the person under surveillance in video frame data received from one or more cameras101-104of the video processing system100and then later failing to detect an image of the person under surveillance in video frames of later-received video data. Thus, in this embodiment, the video processor110withholds assigning resources to detect and track one or more predefined patterns within the received video data until after the video processor110determines that the person under surveillance was in, but has now exited, the video capture area(s) of the system's video camera(s)101-104. Conditioning pattern tracking in this manner enables the video processor110to more efficiently manage processing resources, when necessary.

According to the embodiment ofFIG. 10, the video processing system100, through operation of its communication interface108and video processor110, receives (1001) one or more video data streams from one or more respective cameras101-104within the video processing system100. The video processor110may be programmed to use a streaming control protocol, such as RTSP, to control the video data streams from the cameras101-104when multiple cameras101-104are used.

As each video data stream is received at the video processor110, the video processor110extracts (1003) data representing a set of one or more video frames from the video data based on the video streaming protocol and the video codec used by the respective camera101-104and the video processor110. Responsive to extracting the video frame data, the video processor110determines (1005) whether the video frame data includes data representing an image of the person under surveillance. As discussed above with respect toFIG. 9, the video processor110may compare portions of the video frame data to data representative of a set of human image patterns previously stored in memory114to determine whether a video frame or any portion thereof includes data substantially similar to data representing the person under surveillance. The process flow ofFIG. 10contemplates that the video processing system100may be used to provide suspicious activity alerts to multiple persons under surveillance either simultaneously or at different times. Thus, the system memory114may include one or more databases of human image patterns representing persons who may be subject to surveillance by the video processing system100over time.

When the video frame data from a particular camera101-104, or from multiple cameras101-104over a synchronized time period (e.g., ten seconds or 300 video frames), does not include data representing an image of the person under surveillance, the video processor110extracts (1007) data representing the next set(s) of one or more video frames from the video data stream(s) and determines (1005) whether that video frame data includes data representing an image of the person under surveillance. When the video frame data from a particular camera or set of cameras includes data representing an image of the person under surveillance, the video processor110commences tracking (1009) of the image of the person under surveillance within the video data and extracts (1011) data representing one or more later-in-time sets of video frames from the video data stream or streams. The video processor110analyzes the later-in-time video frame data to determine (1013) whether such video frame data continues to include data representing the image of the person under surveillance. So long as analyzed video frame data continues to include data representing an image of the person under surveillance, the video processor110continues to track (1009) the image of the person under surveillance. The video processor110may also contemporaneously perform the processes described above with respect toFIGS. 2-7to alert the person under surveillance as to suspicious activity, including potential threats, while the person is being actively tracked.

When the later-in-time video frame data is determined (1013) to exclude data representing an image of the person under surveillance, the video processor110determines (1015) whether the video frame data now being received includes data representing one or more predefined patterns. As discussed above with respect toFIGS. 1, 4, and 9, the video processor110may compare portions of the video frame data to data representative of a set of predefined patterns previously stored in memory114to determine whether the video frame or any portion thereof includes data substantially similar to data representing a predefined pattern. The predefined patterns may include, inter alia, object patterns or features, animal patterns or features, features relating to various locations, and general human image patterns or features.

When the video frame data from a particular camera or set of cameras includes data representing one or more predefined patterns, the video processor110commences tracking (1017) of the detected pattern or patterns within video data representing further later-in-time sets of video frames from the video data stream or streams. On the other hand, when the video frame data from a particular camera or set of cameras excludes data representing one or more predefined patterns, the video processor110continues analyzing (1011-1015) received later-in-time video data for data representing an image of the person under surveillance (indicating a return of the person to the video capture area(s) of the camera(s)101-104) and/or data representing one or more predefined patterns.

While an image of the person under surveillance remains absent from the received video data, the video processor110continues tracking the tracked pattern or patterns to determine (1019) whether a tracked pattern is positioned suspiciously relative to either a prior position of the person under surveillance within the video capture area(s) of the system's video camera(s)101-104or an estimated current position of the person under surveillance (e.g., a position at which the person under surveillance was last determined to be prior to leaving the video capture area(s) of the camera(s)101-104, or a position of the person as reported to the video processing system100via an out-of-system means, such as through use of a third party camera or report). To determine whether a tracked pattern is positioned suspiciously relative to a prior position or an estimated current position of the person under surveillance, the video processor110may employ the techniques described above with respect toFIGS. 2-6 and 9. For example, the video processor110may determine whether video frame data, as extracted from received video data, includes data indicative of movement of one or more tracked patterns in a potentially threatening manner relative to the person under surveillance. For instance, the video processor110may compare data representing one or more tracked patterns in one set of video frames to data representing the same tracked pattern(s) in at least one subsequent or other later-in-time set of video frames to determine a motion vector (e.g., velocity) for each such tracked pattern representing movement of the tracked pattern over time. Responsive to determining the motion vector(s), the video processor110may determine whether each motion vector is in a general direction of either a prior position of the person under surveillance or an estimated current position of the person under surveillance. In other words, the video processor110uses the motion vector for a tracked pattern to determine whether the tracked pattern is moving generally toward a prior position or an estimated current position of the person under surveillance.

When the one or more motion vectors are determined to be in a general direction of a prior position or an estimated current position of the person under surveillance, the video processor110may determine that the video frame data includes data indicative of movement of one or more tracked patterns in a potentially threatening manner relative to the person under surveillance. For example, the video processor110may determine whether the motion vector indicates that a tracked pattern is on a track to intercept or pass near a prior position or an estimated current position of the person under surveillance. In such a case, if a tracked pattern is projected to intercept or pass near a prior position or an estimated current position of the person under surveillance within a threshold time period in the future (e.g., within five seconds or 150 video frames), the video processor110may determine that the tracked pattern is positioned suspiciously relative to the person under surveillance. Alternatively, when the one or more motion vectors are determined to be in a general direction of a prior position or the estimated current position of the person under surveillance, the video processor110may estimate, based upon the one or more motion vectors, one or more distances between the one or more tracked patterns and the estimated current position or a prior position of the person. In this case, when a distance between a tracked pattern and the estimated current position or a prior position of the person is less than a threshold (e.g., fifty feet), the video processor110may determine that the tracked pattern is positioned suspiciously relative to the estimated current position or a prior position of the person, and proceed to alert the person.

If a tracked pattern is determined to be positioned suspiciously relative to a prior position or an estimated current position of the person under surveillance, the video processor110alerts (1021) the person under surveillance of a potential threat or other suspicious activity. If, on the other hand, the video processor110never determines (1019) that a tracked pattern is positioned suspiciously relative to either a prior position of the person under surveillance or an estimated current position of the person under surveillance, the absent person monitoring process ends. According to one exemplary embodiment, tracking of predefined patterns further continues if and when the person under surveillance returns into the video capture area(s) of the system's video camera(s)101-104until surveillance is no longer necessary (e.g., the messenger, security guard, or other person under surveillance returns to his or her vehicle and leaves the scene). In other words, the processes described above with respect toFIGS. 2-7continue to be performed when the person under surveillance returns into the video capture area(s) of the system's video camera(s)101-104so as to determine whether any threat may be posed to the person.

The video processor110may alert (1021) the person under surveillance using one or more of a variety of methods, including those described above with respect toFIGS. 2-7. For example, the video processor110may activate a local alert, such as activate an audible and/or visual alarm or send an audio message to a local sound speaker, to notify the person. Alternatively, the video processor110may communicate, via the communication interface108, an alert message to a mobile application or another application (e.g., operating system application) executing on a wireless communication device carried by the person under surveillance (e.g., smartphone, cellular phone, tablet computer, personal digital assistant). In the latter case, the alert message may cause the application to activate an audible alarm and/or a haptic alarm of the wireless communication device and display textual, graphical, and/or other information to notify the person of the suspicious activity. Further, the video processor110may generate a report containing information regarding the one or more tracked patterns and communicate the report, via the communication interface108, to the application executing on the wireless communication device carried by the person under surveillance. The report may include details regarding the suspicious activity and/or a threat assessment as determined and inserted by the video processor110, or another locally or remotely connected processor, based on data representing video frames that include the predefined pattern or patterns. The threat assessment may be a number on a scale (e.g., a scale of one to five), a color code (e.g., red, yellow, green), or any other mechanism for generally or specifically quantifying a threat level associated with the detected suspicious activity, if any.

In the event that the wireless communication device carried by the person under surveillance had previously lost communication contact with the video processing system100(e.g., because the communication device left the coverage area of the video processing system's Wi-Fi network), the video processor110may delay communicating the alert (including any suspicious activity report) to the wireless communication device until after the wireless communication device regains communication contact with the video processing system100. Alternatively or additionally, the video processor110may alert the person under surveillance of detected suspicious activity before the person returns to the video capture area(s) of the video processing system100(i.e., before an image of the person under surveillance reappears in data representing a future set of one or more video frames received from the one or more video cameras101-104) so long as the wireless communication device carried by the person under surveillance continues to remain in communication contact with the video processing system100.

Still further, the video processor110may communicate, via the communication interface108, at least some of the video data from the analyzed video stream(s) (e.g., the last ten seconds or 300 video frames) to a video processing and display application executing on the wireless communication device carried by the person under surveillance. In this case, the application may be configured to automatically play and display the received video to enable the person under surveillance to assess the suspicious activity and react thereto as necessary. According to an alternative embodiment, the video processor110may select sequences of video frames from received video frames to create one or more video clips that include the one or more tracked patterns and insert the video clips into a suspicious activity report communicated to the person under surveillance's wireless communication device. The inserted video clips may then be played by an application installed on or accessible from the person's wireless device. As noted above, such a report may further include details regarding the suspicious activity and/or a threat assessment.

FIG. 11is an alternative embodiment of a process flow diagram1100of steps executed by a video processing system100to alert a person under video surveillance and wearing a body camera as to suspicious activity based on a current location of the person. For this embodiment, the video cameras101-104in the system100include a body camera secured to the body of the person under surveillance. Additionally, the functions of the video processing system100may be performed by one or more video processors110or a set of server instances implementing a cloud-based, video processing architecture800.

According to the process flow1100illustrated inFIG. 11, the video processing system100receives (1101) a stream of video data in real-time or near real-time from the person's body camera. The video data received from the body camera represents images captured by the body camera. The video processing system100extracts (1103) data representing a set of one or more video frames from the received body cam video data and compares (1105) the extracted video frame data to stored data representing image patterns for two or more physical environments. For example, the stored image patterns may include various images that enable the video processing system100to determine whether the person under surveillance is in an indoor environment or an outdoor environment. Thus, the predefined image patterns stored in memory114may include objects such as cubicle walls, reception desks, shopping carts, steering wheels, dashboards, and so forth to facilitate determination of indoor environments (including the interiors of vehicles) and objects such as bushes, flowers, exterior doors, light poles, and so forth to facilitate determination of outdoor environments.

After comparing the body cam video frame data to the stored pattern data, the video processing system100determines (1107) whether the video frame data correlates more closely with a greater urgency environment. The urgency of a particular environment may be established by the video processing system100based upon the operational environment of the system100. For example, where the video processing system100is used to monitor a package delivery service employee or a cash transport service employee, the video processing system100may set outdoor environments as being greater urgency environments than indoor environments. In other words, where the video processing system100is monitoring a package delivery service employee or a cash transport service employee, such an employee typically faces a greater risk of encountering a potential threat outdoors than when the employee is inside a building at which the employee is delivering a package or making a cash pickup. Therefore, for video processing systems100monitoring outdoor threats, the video processing system100may determine that the person under surveillance is in a lesser urgency environment when the video processing system100determines (1107) that the person's body cam video frame data correlates more closely with an indoor environment (i.e., the person's body cam video frame data is determined to include data representing indoor patterns responsive to performing pattern analysis). Conversely, the video processing system100may determine that the person under surveillance is in a greater urgency environment when the video processing system100determines (1107) that the person's body cam video frame data correlates more closely with an outdoor environment (i.e., the person's body cam video frame data is determined to include data representing outdoor patterns responsive to performing pattern analysis).

On the other hand, where the video processing system100is used to monitor persons within a building (e.g., cash office personnel moving cash or casino chips within a casino), the video processing system100may set outdoor environments as being lesser urgency environments than indoor environments. In other words, where the video processing system100is monitoring a cash office employee, such an employee typically faces a greater risk of encountering a potential threat indoors than when the employee is outside having lunch or a cigarette. Therefore, for video processing systems100monitoring indoor threats, the video processing system100may determine that the person under surveillance is in a lesser urgency environment when the video processing system100determines (1107) that the person's body cam video frame data correlates more closely with an outdoor environment (i.e., the person's body cam video frame data is determined to include data representing outdoor patterns responsive to performing pattern analysis). Conversely, the video processing system100may determine that the person under surveillance is in a greater urgency environment when the video processing system100determines (1107) that the person's body cam video frame data correlates more closely with an indoor environment (i.e., the person's body cam video frame data is determined to include data representing indoor patterns responsive to performing pattern analysis).

When the video processing system100determines that the body cam video frame data correlates more closely with stored pattern data representing a greater urgency environment, the video processing system100sends (1109) an alert to the person under surveillance with greater urgency. By contrast, when the video processing system100determines that the body cam video frame data does not correlate more closely with stored pattern data representing a greater urgency environment (or determines that the body cam video frame data correlates more closely with stored pattern data representing a lesser urgency environment), the video processing system100sends (1111) an alert to the person under surveillance with less urgency, if at all.

Greater urgency alerting may refer to the timing, repetition, and form of alerting. For example, greater urgency alerting may include sending an alert immediately upon the video processing system's determination that (a) a tracked potential threat pattern is positioned suspiciously relative to a prior position or an estimated current position of the person under surveillance and (b) the person under surveillance is presently in a greater urgency environment. Greater urgency alerting may also include sending an alert repeatedly over a short period of time (e.g., once per second or once per five seconds) to increase the likelihood that the person under surveillance notices the alert and its urgency. Greater urgency alerting may further include various forms of alerting, such as haptic, textual, visual, and/or audible alerting, to again increase the likelihood that the person under surveillance notices the alert and its urgency.

Lesser urgency alerting may also refer to the timing, repetition, and form of alerting, albeit in a less urgent manner. For example, lesser urgency alerting may include sending an alert some amount of time after (e.g., 10 seconds or more after) the video processing system's determination that (a) a tracked potential threat pattern is positioned suspiciously relative to a prior position or an estimated current position of the person under surveillance and (b) the person under surveillance is not presently in a greater urgency environment. Lesser urgency alerting may also include sending an alert repeatedly over a longer period of time (e.g., once every 10-30 seconds) to remind the person under surveillance of potential suspicious activity. Lesser urgency alerting may alternatively mean not sending an alert at all. For example, when the video processing system determines, through analysis of body cam video data, that the person under surveillance512has returned and is inside his/her vehicle, the video processing system may withhold sending any alert because the person under surveillance is in position to leave the area and any potential suspicious activity.

Lesser urgency alerting may further include various forms of alerting, such as haptic, textual, visual, and/or audible alerting, to again remind the person under surveillance as to the presence of potential suspicious activity, but in a much less overt manner than greater urgency alerting. For instance, lesser urgency alerting may involve haptic and textual alerting only; whereas, greater urgency alerting may involve haptic, textual, and highly audible alerting.

To summarize, according to the logic flow process1100ofFIG. 11, the video processing system100may perform the suspicious activity alerting functions (217,421,919,1021) ofFIGS. 2, 4, 9, and 10with varying degrees of urgency depending upon which physical environment image patterns are present in the monitored person's body cam video frame data. Such urgency-dependent alerting enables the video processing system100to efficiently use processing resources while maintaining the overall safety and security of the person under surveillance.

Two exemplary use cases for applying the processes ofFIGS. 9-11are illustrated inFIGS. 12 and 13. The use case illustrated inFIG. 12is similar to the use case illustrated inFIG. 5, except the person under surveillance (e.g., a cash transport service employee512) is shown without an optional body camera501. According the use case illustrated inFIG. 12, the person under surveillance moves from “Position A” to “Position B” over time (e.g., a few or several seconds) and then potentially further in the general direction of the dashed line projecting from the person under surveillance512. During his or her travel, the person under surveillance moves out of the video capture area525of video camera502, as well as potentially into and out of the video capture areas of one or more of the other video cameras503-510from which the video processor516may be receiving video data streams. During the time that the person under surveillance is moving through video capture areas and/or after he or she is gone (i.e., no longer detectable in video streams received from one or more cameras502-510), the video processor516may continue monitoring for potential suspicious activity, including activity that could pose a potential threat to the person under surveillance when, or as, he or she returns. If suspicious activity is detected, the video processor516may alert the person under surveillance as to such activity while the person remains out of the cameras' video capture areas, so long as the person's mobile device530remains within a coverage range of the video processing system's communication interface (e.g., a Wi-Fi or other short-range interface518or an LTE or other wide area network to which the video processing system's wide area interface520and the person's mobile device530are connected). If the video processor516is unable to communicate with the person's mobile device530upon determining suspicious activity, the video processor516may wait to send an alert until the person's mobile device530reconnects with the video processor516. Alternatively, when circumstances permit and a desire to conserve system resources exists, the video processor516may wait to send an alert until the video processor516re-detects data representing an image of the person under surveillance within video data received from one or more cameras502-510from which the video processor516receives video streams. In other words, the video processor516may wait to send an alert until the person under surveillance returns into one or more video capture areas of the video processing system.

The use case illustrated inFIG. 12may be used to assist in further understanding the suspicious activity detection and alerting process described above with respect toFIG. 9. For the sake of brevity and ease of understanding, operation of the video processing system in connection with the exemplary scenario illustrated inFIG. 12will be limited to considering video images captured by the vehicle-mounted camera502. However, those of ordinary skill in the art will readily recognize and appreciate that the general principles of operation described below and otherwise herein may be applied to systems in which video and/or still images captured by other cameras503-510are considered as part of a suspicious activity determination and alerting process.

In the exemplary scenario depicted inFIG. 12, two potential threats to a person under surveillance (e.g., a cash transport service employee512) are shown for illustrative purposes. The first potential threat is a person527who is moving in the general direction illustrated by the dashed arrow originating from the person527. The second potential threat is a parked car528positioned generally near the ATM514, which may have been a prior position of the employee512before the employee512moved to “Position A” (e.g., where the employee512was previously removing cash or otherwise accessing the interior of the ATM514).

After the video processing system has been activated, the vehicle-mounted camera502begins capturing images from its respective video capture area525and communicating video data representing time-sequenced video frames to the video processor516. The video data may include metadata, such as time stamps (e.g., where the video camera502includes a GPS unit or other accurate time source), or other information based upon which the video frames from the camera502can be time-synchronized. The video processor516receives the video data from the camera502in real time or near real time and may use a streaming control protocol, such as RTSP, to control streams of video data when such data is being received from multiple cameras502-510. The video processor516analyzes the video data in each video frame of the stream received from the camera502to determine whether the video frame data includes data representing one or more predefined patterns (e.g., patterns associated with potential threats or other suspicious activity) and data representing the employee512. A set of predefined patterns may be stored in memory of, or otherwise accessible to, the video processor516. To determine whether a video frame received from the camera502includes a predefined pattern, the video processor516may compare the video frame data to the previously stored data representing the set of predefined patterns. The set of predefined patterns may include, for example, the outline or other features of a human body or a portion thereof, the outline or other features of one or more predetermined objects (such as a firearm, knife, bat, club, TASER, or other object that could be used as a weapon), and/or the outline or other features of a vehicle. The video processor516may be programmed to update and/or expand the stored predefined pattern data by applying machine learning techniques, such as supervised learning techniques (e.g., classification and/or regression algorithms), unsupervised learning techniques (e.g., association, clustering, and/or dimensionality reduction algorithms), and/or reinforcement learning techniques, to video data received by the video processor516from the camera502over time.

The video processor516also analyzes the video data in each video frame of the stream received from the camera502to determine whether the video frame data includes data representing the employee512. Data representing employees or other persons to be monitored by the video processing system may be stored in the memory of, or a memory otherwise accessible to, the video processor516. To determine whether a video frame received from the camera502includes data representing the employee512, the video processor516may compare the video frame data to previously stored image data representing employees.

When the video processor516has determined that at least a portion of the video frame data includes data substantially similar to stored data representing one or more predefined patterns, the video processor516may determine that the video frame data includes predefined pattern data. As discussed above with respect toFIG. 2, the video processor516may determine video data is substantially similar to data representing a particular predefined pattern where the video data has at least a fifty percent (50%) correspondence or correlation with the data representing the particular predefined pattern within a stored set of predefined patterns. In an alternative embodiment, the video processor516may determine whether the video frame data includes data representing a particular predefined pattern by comparing combinations of positions and velocity vectors for multiple simultaneously-tracked patterns to prestored reference combinations of positions and velocity vectors and assigning a threat probability for each tracked pattern based on the degree of correspondence or correlation between the combination of position and velocity vector for each tracked pattern and the combinations of positions and velocity vectors for one or more stored predefined patterns.

When the video processor516has determined that at least a portion of the video frame data includes data substantially similar to stored image data representing the employee512, the video processor516may determine that the video frame data includes employee pattern data. The video processor516may determine video data is substantially similar to stored image data representing the employee512where the video data has at least a fifty percent (50%) correspondence or correlation (and more preferably, at least a seventy-five percent (75%) correspondence or correlation) with stored image data for a particular employee.

When the video processor516has determined that at least a portion of the video frame data includes data representing one or more predefined patterns and data representing the employee512, the video processor516commences tracking the predefined pattern and the employee512independently within the video data received from the video camera502. Pattern and employee tracking may be performed on a video frame-by-video frame basis or on any other periodic or aperiodic basis (e.g., every other video frame, every fifth video frame, every third video frame during daylight hours, but every video frame during nighttime hours, and so forth). According to one exemplary embodiment, the video processor516may define a bounding area for each tracked pattern and a bounding area for the tracked employee512. The video processor516initiates tracking to monitor for changes to the bounding areas over time, especially within the camera's video capture area525. For example, once a tracked pattern and the employee pattern are detected in video data representing a video frame, the video processor516may position one shape as a boundary around the tracked pattern and the same shape or a different shape as a boundary around the employee pattern to form trackable areas for purposes of reducing the amount of processing resources necessary to track the pattern and the employee512. In other words, when the employee512and a particular predefined pattern have been detected within a video frame, the patterns may be separately “bounded” within respective reference areas to make evaluating the pattern's and employee's positioning over multiple video frames less processing intensive.

After pattern and employee tracking have been commenced, the video processor516determines whether data representing one or more subsequent video frames includes data representing the tracked pattern and data representing the employee512. In other words, after pattern and employee tracking has commenced, the video processor516analyzes some or all of the data representing video frames subsequent in time to the video frame that triggered the tracking to determine whether such data includes the tracked pattern and employee512. Such analysis may include comparing some or all of the video data representative of a subsequent video frame to previously stored data representing the predefined pattern and the employee512or comparing some or all of the video data representative of a subsequent video frame to data representing the predefined pattern and the employee512as detected in a prior video frame.

According to one exemplary embodiment, the video processor516analyzes video frame data on a periodic basis after pattern tracking has commenced. For example, the video processor516may analyze data representing ten consecutive video frames where the camera502supplying the video data is capturing images at a rate of thirty frames per second (30 fps). In such a case, the video processor516analyzes received video data every 333 milliseconds to determine whether such data includes the tracked pattern and the employee512after tracking has commenced. As another example, the video processing system may analyze data representing fifteen consecutive video frames where the camera502supplying the video data is capturing images at a rate of sixty frames per second (60 fps). In this particular case, the video processor516may analyze received video data every 250 milliseconds to determine whether such data includes the tracked pattern and employee512after tracking has been commenced. The quantity of video frames analyzed by the video processing system may be selected based on several factors, including camera video quality, location and/or size of video capture area, positioning of the person under surveillance within the video capture area, quantity and type of physical and natural structures in or near the video capture area, and so forth.

When data representing one or more subsequent video frames ceases to include data representing the employee512but continues to include data representing the tracked pattern, the video processor516continues to track the tracked pattern in subsequent or other later-in-time video frame data to determine whether the tracked pattern is or becomes positioned suspiciously relative to a prior position of the employee512or a current estimated position of the employee512. According to one exemplary embodiment, the video processor516may determine whether the analyzed data includes data indicative of positioning of the tracked pattern (or its respective bounding area) near, or movement of the tracked pattern toward, a prior position of the employee512(e.g., near the ATM514or near the rear of the vehicle522) or a current estimated position of the employee512. For example, the video processor516may determine a motion vector for the tracked pattern over several received video frames to determine whether the tracked pattern's path of travel will pass near a prior position or a current estimated position of the employee512. The video processor516may also determine a motion vector for the employee512prior to the employee512leaving the video capture area525of the camera502. The video processor516may then analyze the paths of travel of the tracked pattern and the employee512based on the motion vectors to determine whether the tracked pattern's path will intersect the employee's path and, if so, where such intersection will take place (which could be at an interpolated position outside the video capture area525of the video camera502). Alternatively, where a tracked pattern is determined to be following the general path of movement of the employee512and the tracked pattern exits the video capture area525of the video camera502near where the employee512previously exited such area525, the video processor516may determine that the tracked pattern is positioned suspiciously relative to the estimated current position of the employee512. For the purpose of estimating the employee's current position, the video processor516may select a position in a general region of the camera's video capture area525where the employee512was last detected in a video frame or where the employee's motion vector would have placed the employee when he/she left the camera's video capture area525. With respect to a tracked pattern that remains stationary, such as the pattern of the parked car528, the video processor516may continue tracking the pattern for movement and/or analyzing video frame data extracted from the camera's video stream to assess whether one or more additional predefined patterns may be present near the stationary pattern, all while the employee512remains outside the video capture area525of the camera502.

If the video processor516determines that a tracked pattern is or becomes positioned suspiciously relative to a prior position of the employee512or a current estimated position of the employee512, the video processor516sends an alert to the mobile device530carried by the employee512to inform the employee512of such suspicious activity. The alert enables the employee512to take necessary precautions to prepare for and/or avert a potential threat either where the employee512is currently located or prior to returning to or near any position or location previously occupied by the employee512while in the video capture area525of the camera502supplying real-time or near real-time video data to the video processor516.

The use case illustrated inFIG. 12may also be used to facilitate a better understanding of the suspicious activity detection and alerting process described above with respect toFIG. 10. More particularly, the situation illustrated inFIG. 12provides an exemplary backdrop with which to describe how a video processing system may automatically monitor for suspicious activity after a person under surveillance (e.g., a cash transport service employee512) exits one or more video capture areas of cameras supplying video streams to the system's video processor516and alert the person under surveillance when such suspicious activity is detected. For the sake of brevity and ease of understanding, operation of the video processing system in connection with the exemplary scenario illustrated inFIG. 12will again be limited to considering video images captured by the vehicle-mounted camera502. However, those of ordinary skill in the art will readily recognize and appreciate that the general principles of operation described below and otherwise herein may be applied to systems in which video and/or still images captured by other cameras503-510are considered as part of a suspicious activity determination and alerting process.

As noted above, two potential threats to the cash transport service employee512are shown for illustrative purposes. The first potential threat is a person527who is moving in the general direction illustrated by the dashed arrow originating from the person527. The second potential threat is a parked car528positioned generally near the ATM514, which have been a prior position of the employee512before the employee512moved to “Position A” (e.g., where the employee512was previously removing cash or otherwise accessing the interior of the ATM514).

After the video processing system has been activated, the vehicle-mounted camera502begins capturing images from its respective video capture area525and communicating video data representing time-sequenced video frames to the video processor516. The video data may include metadata, such as time stamps (e.g., where the video camera502includes a GPS unit or other accurate time source), or other information based upon which the video frames from the camera502can be time-synchronized. The video processor516receives the video data from the camera502in real time or near real time and may use a streaming control protocol, such as RTSP, to control streams of video data when such data is being received from multiple cameras502-510. The video processor516analyzes the video data in each video frame of the stream received from the camera502to determine whether the video frame data includes data representing the employee512. Data representing employees or other persons to be monitored by the video processing system may be stored in the memory of, or memory otherwise accessible to, the video processor516. To determine whether a video frame received from the camera502includes data representing the employee512, the video processor516may compare the video frame data to previously stored image data representing company employees.

When the video processor516has determined that at least a portion of the video frame data includes data substantially similar to stored image data representing the employee512, the video processor516may determine that the video frame data includes employee pattern data. The video processor516may determine video data is substantially similar to stored image data representing the employee512where the video data has at least a fifty percent (50%) correspondence or correlation (and more preferably, at least a seventy-five percent (75%) correspondence or correlation) with stored image data for a particular employee.

When the video processor516has determined that at least a portion of the video frame data includes employee pattern data, the video processor516commences tracking the employee512within the video data received from the video camera502. Employee tracking may be performed on a video frame-by-video frame basis or on any other periodic or aperiodic basis (e.g., every other video frame, every fifth video frame, every third video frame during daylight hours, but every video frame during nighttime hours, and so forth). According to one exemplary embodiment, the video processor516may define a bounding area for the tracked employee image pattern. In such a case, the video processor516initiates tracking to monitor for changes to the bounding area over time, especially within the camera's video capture area525. For example, once employee pattern data is detected in video data representing a video frame, the video processor516may position one shape as a boundary around the employee image pattern to form a trackable area for purposes of reducing the amount of processing resources necessary to track the employee512. In other words, when an image of the employee512has been detected within a video frame, the employee image pattern may be “bounded” within a reference area to make evaluating the employee's positioning over multiple video frames less processing intensive.

After employee tracking have been commenced, the video processor516determines whether data representing one or more subsequent video frames includes employee pattern data. In other words, after employee tracking has commenced, the video processor516analyzes some or all of the data representing video frames subsequent in time to the video frame that triggered the tracking to determine whether such data includes the employee image pattern. Such analysis may include comparing some or all of the video data representative of a subsequent video frame to previously stored image data for the employee512or comparing some or all of the video data representative of a subsequent video frame to data representing the image of the employee512as detected in a prior video frame.

According to one exemplary embodiment, the video processor516analyzes video frame data on a periodic basis after employee image pattern tracking has commenced. For example, the video processor516may analyze data representing ten consecutive video frames where the camera502supplying the video data is capturing images at a rate of thirty frames per second (30 fps). In such a case, the video processor516analyzes received video data every 333 milliseconds to determine whether such data includes data representing an image of the employee512. As another example, the video processing system may analyze data representing fifteen consecutive video frames where the camera502supplying the video data is capturing images at a rate of sixty frames per second (60 fps). In this particular case, the video processor516may analyze received video data every 250 milliseconds to determine whether such data includes data representing an image of the employee512. The quantity of video frames analyzed by the video processing system may be selected based on several factors, including camera video quality, location and/or size of video capture area, positioning of the employee512within the video capture area525, quantity and type of physical and natural structures in or near the video capture area525, and so forth.

When data representing one or more subsequent video frames is determined to exclude data representing an image of the employee512, the video processor516begins analyzing subsequent video frames for data representing one or more predefined patterns (e.g., patterns associated with potential threats or other suspicious activity). As discussed above, a set of predefined patterns may be stored in memory of, or otherwise accessible to, the video processor516. To determine whether a video frame received from the camera502includes a predefined pattern, the video processor516may compare the video frame data to the previously stored data representing the set of predefined patterns. The video processor516may be programmed to update and/or expand the stored predefined pattern data by applying machine learning techniques, such as supervised learning techniques (e.g., classification and/or regression algorithms), unsupervised learning techniques (e.g., association, clustering, and/or dimensionality reduction algorithms), and/or reinforcement learning techniques, to video data received by the video processor516from the camera502over time.

When the video processor516has determined that data representing the employee512is absent from received video frame data and at least a portion of the received video frame data includes data substantially similar to stored data representing one or more predefined patterns, the video processor516may determine that the video frame data includes predefined pattern data. In other words, the video processor tracks one or more predefined patterns in video data received from the video camera502after the employee512leaves the video capture area525of the camera502and for the time period that the employee512remains absent from the video capture area525of the camera502. As discussed above, the video processor516may determine video data is substantially similar to predefined pattern data where the video data has at least a fifty percent (50%) correspondence or correlation with data for a particular predefined pattern within the stored set of predefined patterns. In an alternative embodiment, the video processor516may determine whether the video frame data includes predefined pattern data by comparing combinations of position and velocity vectors for multiple simultaneously-tracked patterns to prestored reference combinations of position and velocity vectors and assigning a threat probability for each tracked pattern based on the degree of correspondence or correlation between the combination of position and velocity vector for the tracked pattern and one or more prestored reference combinations of positions and velocity vectors.

The video processor516continues to track the tracked pattern in subsequent or other later-in-time video frame data to determine whether the tracked pattern is or becomes positioned suspiciously relative to a prior position of the employee512or a current estimated position of the employee512. According to one exemplary embodiment, the video processor516may determine whether the analyzed data includes data indicative of positioning of the tracked pattern (or its respective bounding area) near, or movement of the tracked pattern toward, a prior position of the employee512(e.g., near the ATM514or near the rear of the vehicle522) or a current estimated position of the employee512. For example, the video processor516may determine a motion vector for the tracked pattern over several received video frames to determine whether the tracked pattern's path of travel will pass near a prior position or a current estimated position of the employee512. The video processor516may also determine a motion vector for the employee512prior to the employee512leaving the video capture area525of the camera502. The video processor516may then analyze the paths of travel of the tracked pattern and the employee512based on the motion vectors to determine whether the tracked pattern's path will intersect the employee's path and, if so, where such intersection will take place (which could be at an interpolated position outside the video capture area525of the video camera502). Alternatively, where a tracked pattern is determined to be following the general path of movement of the employee512and the tracked pattern exits the video capture area525of the video camera502near where the employee512previously exited such area525, the video processor516may determine that the tracked pattern is positioned suspiciously relative to the estimated current position of the employee512.

For the purpose of estimating the employee's current position, the video processor516may select a position in a general region of the camera's video capture area525where the employee512was last detected in a video frame or where the employee's motion vector would have placed the employee when he/she left the camera's video capture area525. With respect to a tracked pattern that remains stationary, such as the pattern of the parked car528, the video processor516may continue tracking the pattern for movement and/or analyzing video frame data extracted from the camera's video stream to assess whether one or more additional predefined patterns may be present near the stationary pattern, all while the employee512remains outside the video capture area525of the camera502.

If the video processor516determines that a tracked pattern is or becomes positioned suspiciously relative to a prior position of the employee512or a current estimated position of the employee512, the video processor516sends an alert to the mobile device530carried by the employee512to inform the employee512of such suspicious activity. The alert enables the employee512to take necessary precautions to prepare for and/or avert a potential threat either where the employee512is currently located or prior to returning to or near any position or location previously occupied by the employee512while in the video capture area525of the camera502supplying real-time or near real-time video data to the video processor516.

FIG. 13illustrates an exemplary use case to assist in further understanding the suspicious activity alerting process described above with respect toFIG. 11, where the person under surveillance (in this case, employee512) is wearing a body camera1301. The use case illustrated inFIG. 13is similar to the use case illustrated inFIG. 5, except that the employee512has repositioned outside the video capture areas of the cameras502-507supplying streaming video to the video processor516. Additionally, in this use case, the employee's body camera1301captures video data from its associated video capture area1305and communicates a video stream of the captured video data to the video processor516via its own communication interface (e.g., Wi-Fi or LTE) or via a communication interface of the person's mobile device530(e.g., via Wi-Fi or other short-range communication from the body cam501to the mobile device530and then via Wi-Fi, LTE or another communication protocol from the mobile device530to the video processor516).

Applying the alerting process ofFIG. 11to the exemplary use case illustratedFIG. 13, the video processor516receives video data streams from one or more cameras502-507monitoring the general area in which the employee512was previously located, as well as a video data stream from the employee's body cam1301. The video processor516extracts data representing sets of one or more video frames from the video data received from the area cameras (e.g., camera502) and the video data received from the employee's body cam1301. For each extracted video frame of video data received from an area camera502, the video processor516compares the extracted data to stored data representing suspicious activity image patterns. For each extracted video frame of video data received from the employee's bodycam1301, the video processor516compares the extracted data to stored data representing two or more physical environments. For example, the environment-related stored image patterns may include various images that enable the video processor516to determine whether the employee512is in an indoor environment or an outdoor environment. Thus, the prestored environment-related image patterns may include objects such as cubicle walls, reception desks, shopping carts, steering wheels, vehicle dashboards, and so forth to facilitate determination of indoor environments (including the interiors of vehicles) and objects such as bushes, flowers, trees, shrubs, exterior doors, light poles, and so forth to facilitate determination of outdoor environments.

In the use case illustrated inFIG. 13, the employee's body cam1301captures an image of a bush1305and sends the image to the video processor516within one or more frames of video data. Upon receiving such video data from the employee's body cam1301, the video processor516may determine that the employee512is currently in an outdoor environment responsive to comparing the received video frame data to stored data representing outdoor environment image patterns, including image patterns for various forms of bushes, trees, plants, shrubs, and/or other forms of greenery. The video processor516may determine that the employee512is currently in an outdoor environment when the received video frame data correlates or corresponds closely with (e.g., to within a correlation of at least 50% of) a prestored outdoor image pattern, such as a bush. The video processor516may also determine that an outdoor environment is a greater urgency environment where, as in the illustrated use case, the area camera502is monitoring an outdoor environment for suspicious activity.

Where an outdoor environment is considered to be a greater urgency environment, the video processor516determines that the employee512is currently in an outdoor environment, and the video processor516determines that video data received from an area camera502includes data representing a predefined pattern positioned suspiciously relative to a prior position of the employee512(e.g., a position at which the employee512was located while previously within the video capture area525of the area camera502) or a current estimated position of the employee512(e.g., a position at which the employee512was approximately located when leaving the video capture area525of the area camera502), the video processor516sends an alert to the mobile device530of the employee512(e.g., to an application executing on the mobile device530). As discussed above, the alert may be a textual or graphical message (including, for example, a map image showing where suspicious activity has been detected), an audible sound or recorded message, a haptic alert, or any combination thereof. Also, because the employee512has been determined to be in a greater urgency environment in the use case ofFIG. 13, the video processor516sends the alert according to a greater urgency protocol, which may include repeated transmissions of the alert at a much faster rate, on average, than under a lesser urgency protocol. The transmission rate of the alert may increase over time under the greater urgency protocol and may be accompanied by increasing strengths or emphasis in the audible and/or haptic nature of the alert. In other words, when the employee512is determined to be in a greater urgency environment when suspicious activity is detected, the video processor516executes a greater urgency protocol in an attempt to expeditiously alert the employee512as to suspicious activity possibly occurring in the geographic area previously exited by the employee512and to which the employee512is likely intending to return. The alerts are preferably sent to the employee512at the employee's current location (which may require wide area communications where the employee512has left the coverage area of the system's short-range wireless communications subsystem (e.g., Wi-Fi, Bluetooth, or otherwise)). Where transmission of an alert to the employee's current location is not possible (e.g., because the employee's mobile device530is out of range), the video processor516may delay transmission of the alert until the employee's wireless device530re-enters the coverage area of the system's short-range wireless communications subsystem.

Therefore, the video processor516may, upon detecting suspicious activity in a monitored area, alert a person under surveillance who is currently absent from the monitored area as to such suspicious activity by using different urgency protocols depending upon the physical environment in which the person under surveillance is currently located. To assess the surveilled person's current physical environment, the video processor516analyzes video data received from the monitored person's body camera1301and compares image patterns represented by such data to stored image patterns of different physical environments (e.g., indoor and outdoor environments). Depending upon, among other things, the relationship between the monitored area and the type of environment in which the person under surveillance is currently located, the video processor516selects an urgency protocol with which to send an alert, if any, to the person under surveillance informing the person as to potential suspicious activity in the monitored area.

Referring now toFIG. 14, there is depicted an electrical block diagram of a video processing system1400in accordance with an exemplary alternative embodiment of the present disclosure. This embodiment of the video processing system1400is similar to the embodiment of the video processing system100illustrated inFIG. 1, except that this embodiment further includes one or more optional motion-sensing subsystems1401and one or more optional microphones1402or other audio-receiving devices (e.g., transducers). Thus, according to this embodiment, the video processing system1400includes, inter alia, the one or more cameras101-104(four shown for illustration), a video processing apparatus1406, one or more optional motion-sensing subsystems1401, and one or more optional microphones1402. The video processing apparatus1406may include, inter alia, the communication interface108, one or more processors1410(one shown for illustration), and optional memory114. The motion-sensing subsystem1401may include one or more types of motion sensors, such as two-axis or three-axis accelerometers, gyroscopes, magnetometers, GPS units, and/or composite inertial measurement units. The processor1410may include one or more video processors110as described above with respect toFIG. 1. Alternatively, when the video processing apparatus1406is configured to receive and process audio data from one or more system microphones1402, the processor1410may include one or more video processors configured to analyze and process such audio data or may further include separate audio and video processors. The video processing system1400may be contained within a single enclosure, such as within a body camera501or a vehicle camera502, or may be distributed, such illustrated above with regard toFIG. 5and below with regard toFIG. 18.

Where the video processing apparatus1406is collocated with a local alerting mechanism112, such mechanism112may include an audio speaker, a horn, a haptic or tactile alerting device, one or more lights or lighting units, and/or a video display. The local alerting mechanism112is intended to quickly alert the person under surveillance as to the presence of a possible threat when the video processing apparatus110, as part of the overall video processing system1400, determines from received video data (and optionally motion data) that such a potential threat is present. Where a local alerting mechanism is not present or desired, the processor1410may communicate an alert signal to a remote alerting device, such as a wireless communication device carried by the person under surveillance, by way of the communication interface108.

Operation of the alternative video processing system1400ofFIG. 14will be generally described below with respect toFIG. 15. Further alternative operations of the video processing system1400will be described more particularly below with respect toFIGS. 16 and 17, as well as in connection with some exemplary use cases as illustrated inFIGS. 18 and 23-26. An optional cloud-based implementation/architecture, such as the architecture described above with respect toFIG. 8, may also be used to implement the video processing apparatus1406of the video processing system1400depicted inFIG. 14, provided that the cloud-based architecture includes appropriate software and hardware modifications to perform the functions of the video processing system1400as described below.

Referring now toFIG. 15, there is shown a process flow diagram1500of steps executed by a video processing system to detect suspicious activity in a general vicinity of a person or object, such as a motor vehicle, based on real-time or near real-time video analysis in accordance with another exemplary embodiment of the present disclosure. The steps of the process flow diagram1500may be performed by the video processing system (and primarily by its video processor) through execution of stored operating instructions (firmware and/or software). By way of example, but not limitation, the suspicious activity detection process flow ofFIG. 15is described below with reference to the video processing system1400ofFIG. 14.

The process flow begins when one or more cameras101-104capture images within video capture areas defined by the cameras' respective fields of view. The cameras101-104generate encoded video data streams from the images and divide the video streams into a series of time-sequenced or time-stamped video frames according to the video streaming protocol being used. In one exemplary embodiment, the camera or cameras101-104are configured to capture images and encode video data at a rate of at least 30 frames per second. The video streams are communicated to the video processing apparatus1406for video analysis processing.

When the system includes one or more microphones1402, such microphones1402may form part of or be collocated with the cameras101-104. The microphones capture audio in the video capture areas of the video cameras101-104and potentially outside such areas as well. The audio from any particular microphone1402may be sampled, digitized, and time-synchronized with video data captured by the microphone's associated camera101-104. A processor may be included in the camera101-104and perform such functions, as well as divide and map the digitized audio with respective video frames.

The cameras' fields of view are such that the cameras101-104capture video from video capture areas proximate (generally near) a person under surveillance while the suspicious activity process is being executed. For example, one camera101may be a low profile or other styled body camera secured to the chest, arm, helmet, back, shoulder, neck, or other area of the person under surveillance, such as through use of a strap or belt, vest, holster, or other device. The camera101may be forward-facing or rearward-facing, as determined to be necessary by the wearer (person under surveillance). Such a camera101may, depending on its capabilities, capture images extending out several feet or meters (e.g., 150 feet or 50 meters or more) as referenced from the person's current position.

Another one or more cameras102-104may be mounted at predetermined locations on a vehicle (e.g., truck, car, boat, bus, motorcycle, and so forth) that transported the person to his or her current location or that is otherwise positioned near the person under surveillance. The positioning of the cameras102-104on the vehicle may be such that the cameras102-104captures images of the person and his surroundings at locations where the person is and/or is expected to be after stopping the vehicle. For example, where the person under surveillance is a police officer, the vehicle-mounted cameras102-104may be mounted to or included with the vehicle at one or more positions, such as on the driver's side of the vehicle (e.g., adjacent the driver's side door or on the driver's side of the hood), on the passenger's side of the vehicle, on a rear-view mirror assembly of the vehicle, on the windshield or rear window of the vehicle (e.g., with one or more suction cups or hook-and-loop fasteners) and/or on the back of the vehicle (e.g., above and/or adjacent to the rear doors or on the trunk). Depending on the types of cameras102-104utilized, the cameras102-104may capture images extending out several feet or meters (e.g., 150 feet or 50 meters or more) from the vehicle.

Other cameras may be mounted at fixed locations near the location of the person. For example, cameras may be mounted to buildings, canopies, trees, light poles, or other objects near the general location of the person under surveillance. Due to their positioning, such cameras may capture images within a much wider video capture area than the video capture areas of body-mounted or vehicle-mounted cameras.

The video processing apparatus1406receives (1501) a video data stream from each camera101-104in real time or near real time via the apparatus' communication interface108. In other words, each camera101-104captures images, encodes the images into video data containing time-sequenced video frames, and communicates the video data to the video processing apparatus1406as a stream of video frames in accordance with a video streaming protocol, without intentionally delaying the flow of video data any more than is necessary. That is, neither the video processing apparatus1406nor the video processing system1400as a whole introduces any delays other than normal processing and communication delays. Use of the terms “real time,” “real-time,” “near real-time,” and “near real time” take into account such inherent delays. The processor1410may use one or more video streaming control protocols, such as RTSP 2.0 or any successor thereof, to control the delivery of video data from the cameras101-104. According to one exemplary embodiment, the cameras101-104and the processor1410use video transport and streaming protocols, such as RTMP and RTP or any successors thereof, to transmit and receive video data in real time or near real time.

In addition to receiving the video data streams, the video processing apparatus1406may optionally receive (1503) synchronized audio data streams from the camera or other system microphones1402in real time or near real time. As discussed above, the raw audio data may be pre-processed by the camera processor (or another processor) to convert the raw audio to digital audio data processable by the video processing apparatus1406. Where the processor1410uses RTMP and RTP for controlling video streaming from multiple cameras101-104, the processor1410may also use such protocols to control audio streaming from multiple microphones1402.

As the video data from a particular camera101-104is received at the video processing apparatus1406, the apparatus' processor1410extracts (1505) data representing a video frame from the video data based on the video streaming protocol and the video codec (e.g., H.264 or H.265) used by the camera101-104and the processor1410, and determines (1507) whether the video frame data includes data representing one or more predefined image patterns. For example, the processor1410may compare portions of the video frame data to data representing a set of predefined patterns (e.g., potential threat patterns) previously stored in memory114to determine whether the video frame data or any portion thereof includes data substantially similar to data representing a stored image pattern. Video data may be considered substantially similar to stored image pattern data where the video data has at least a fifty percent (50%) correspondence or correlation with the stored image pattern data. Additionally or alternatively, the processor1410may execute machine learning and computer vision algorithms to perform object detection, face detection, face recognition, summarization, threat detection, natural language processing, sentiment analysis, traffic monitoring, intention detection and so on to evaluate whether the video frame data includes data representing one or more of the predefined and stored image patterns.

The set of predefined image patterns may include, for example, the outline or other features of a human body or a portion thereof, the outline or other features of one or more predetermined objects (such as a firearm, knife, bat, club, TASER, or other object that could be used as a weapon), the outline or other features of a vehicle (e.g., vehicle door in opened position, vehicle door in closed position, windshield, rear window, rear-view mirror, etc.), and/or the features of one or more types of locations. The processor1410may be programmed to update and/or expand the stored image pattern data by applying machine learning techniques, such as supervised learning techniques (e.g., pattern recognition, object classification, and/or regression algorithms), unsupervised learning techniques (e.g., association, clustering, and/or dimensionality reduction algorithms), and/or reinforcement learning techniques, to video data received by the processor1410over time.

Where the video processing apparatus1406receives video data streams from multiple sources (e.g., cameras101-104), the processor1410analyzes each video stream separately and may use metadata within the video streams to time-synchronize the streams. The metadata for each video data stream may include a time-and-date stamp, which permits the processor1410to align the video frames of the video data streams even though such streams may be received at different times by the video processing apparatus1406.

When the video frame data from a particular camera101-104does not include data representing a predefined image pattern, the processor1410extracts (1509) data representing the next video frame from the video data stream and determines (1507) whether that video frame data includes data representing one or more of the predefined image patterns. When the video frame data from a particular camera includes data representing at least one predefined image pattern (e.g., a pattern match or correlation occurs), the processor1410commences (1511) tracking of the detected image pattern or patterns within the video data.

According to one exemplary embodiment, image pattern tracking continues for a predetermined period of time over a predetermined set of subsequent or other later-in-time video frames, which period may be extended by the processor1410based on pre-established extension criteria. The set of later-in-time video frames may include contiguous video frames, periodically positioned video frames (e.g., every other video frame in the set, every third video frame in the set, and so forth), or randomly selected video frames within the image tracking time period. For example, where the video data was captured by the camera101-104at 30 frames per second, image pattern tracking may continue for a fraction of a second (e.g., 333 milliseconds or 500 milliseconds) or for multiple seconds as may be selected by the system operator. As a further example, where image pattern tracking is to be performed on contiguous video frames for a period of 500 milliseconds after a predefined image pattern has been detected and the video data includes 30 frames per second, image pattern tracking may be programmed to occur for data representing fifteen consecutive video frames.

As synched audio data is received at the processor1410from a particular source (e.g., microphone1402), the processor1410extracts (1505) data representing a video frame's worth of audio data based on the audio streaming protocol and the audio codec (e.g., Advanced Audio Coding (AAC)) used by the microphone1402(or the camera101-104that includes the microphone1402) and the processor1410. The processor1410then determines (1513) whether the synched audio data includes data representing one or more predefined audio patterns. For example, the processor1410may compare portions of the received audio data to data representing a set of predefined audio patterns previously stored in memory114to determine whether the received audio data includes data substantially similar to data representing a stored audio pattern. Received audio data may be considered substantially similar to stored audio data where the received audio data has at least a fifty percent (50%) correspondence or correlation with a stored audio data pattern. Additionally or alternatively, the processor1410may execute machine learning and audio analysis algorithms to perform speech detection and analysis, background noise detection, and so on to evaluate whether the received audio data includes data representing one or more predefined audio patterns.

The set of predefined audio patterns may include, for example, gunshot sound patterns, breaking glass sound patterns, squealing tire sound patterns, aggressive speech patterns, and so forth. The processor1410may be programmed to update and/or expand the stored audio pattern data by applying machine learning techniques, such as supervised learning techniques, unsupervised learning techniques, and/or reinforcement learning techniques, to audio data received by the processor1410over time.

When the processor1410determines that received audio data includes data representing one or more of the predefined audio patterns, the processor1410may insert (1515) a digital marker within the corresponding video data at the time at which the detected audio pattern commenced. The processor1410may then store (1517) the marker within the video data so that the marker is detectable by viewers of the associated video or detection software at a later time. The marker may provide an indicator to those viewing the video to focus attention, such as when viewing the video as part of a criminal investigation. The marker may also function as a searching aid to enable persons viewing the associated video or marker detection software to quickly skip to the time at which a detected audio pattern commenced.

After image pattern tracking has commenced, the processor1410extracts (1519) data representing a next set of one or more video frames from the video data stream (e.g., a set of video frames occurring later in time than the set of video frames that caused commencement of image pattern tracking) and determines (1521) whether the video frame data includes data representing one or more of the tracked image patterns. For example, the processor1410may compare portions of the video frame data to data representing the tracked pattern or patterns to determine whether the video frame or any portion thereof includes data substantially similar to data representing a tracked pattern. Video data may be considered substantially similar to tracked pattern data where the video data has at least a fifty percent (50%) correlation with the tracked pattern data. Additionally or alternatively, the processor1410may execute machine learning and computer vision algorithms to perform object detection, face detection, face recognition, summarization, threat detection, natural language processing, sentiment analysis, traffic monitoring, intention detection and so on to evaluate whether the video frame data includes data representative of a tracked pattern.

If data representing a tracked pattern is found in the data representing one or more subsequent video frames, the processor1410determines (1523) whether the tracked pattern has changed position in a suspicious manner. Otherwise, the processor1410extracts (1505) the next set of one or more video frames from the video data and the process repeats from decision block1507.

To determine whether the tracked pattern has changed position in a suspicious manner, the processor1410analyzes movement of the tracked pattern over multiple video frames. For example, the processor1410may determine, based on the tracking, whether the tracked pattern is moving toward the person under surveillance, moving away from the person under surveillance, falling down, getting up, moving left, moving right, and so forth. According to one exemplary embodiment, the video processor1410may utilize a process similar to the one described above with respect toFIG. 6to analyze video data from a camera (e.g., camera101) positioned in or on the motor vehicle that transported the person under surveillance to the current location. The processor1410may determine from the video data analysis that the tracked pattern is approaching or moving away from the person under surveillance and/or the stopped motor vehicle, either of which may be deemed a suspicious change of position of the tracked pattern depending on other factors, such as the position and rate of approach or departure, and/or the presence of another predefined pattern in the video data (e.g., the pattern for a weapon). The video processor1410may alternatively or additionally determine from the video data analysis that a tracked pattern, such as a door or window, has opened or closed, which may be considered suspicious depending on the context as determined by the processor1410based on other image patterns detected in the video data and/or audio patterns detected in received audio data.

Exemplary processes for determining whether a tracked image pattern has changed position in a suspicious manner are described below with respect toFIGS. 16 and 17. Such processes relate generally to determining whether an approaching object (FIG. 16) or a departing object (FIG. 17) may be considered suspicious. A further exemplary process for determining whether a tracked image pattern has changed position in a suspicious manner is described below with respect toFIG. 24. The process described with respect toFIG. 24relates generally to determining whether a man-down condition has occurred or is occurring.

When the processor1410determines that one or more tracked patterns have changed position in a suspicious manner, the processor1410alerts (1525) the person under surveillance and/or a third party (e.g., an emergency management system) as to the suspicious activity. For example, the processor1410may activate a local alert, such as activate an audible and/or visual alarm or send an audio message to a local sound speaker, to notify the person under surveillance (e.g., the police officer or officers on scene). Alternatively, the processor1410may communicate, via the communication interface108, an alert message to a mobile application executing on a wireless communication device carried by the person under surveillance (e.g., smartphone, cellular phone, tablet computer, personal digital assistant). In the latter case, the alert message may cause the mobile application to activate an audible alarm and/or a haptic alarm of the wireless communication device to notify the person of the potential threat. Still further, the processor1410may communicate, via the communication interface108, at least some of the video data from the analyzed video stream (e.g., the last ten seconds or 300 video frames) to a mobile video processing and display application executing on a wireless communication device carried by the person under surveillance. In this case, the mobile application may be configured to automatically play and display the received video to enable the person under surveillance to assess the potential threat and react thereto as necessary. Still further, the processor1410may communicate, via the communication interface108, an emergency message to a remote emergency management system to inform an operator of the system (e.g., a police office or 911 emergency operator) as to potential suspicious activity at the location of the person under surveillance, including, without limitation, the possibility of a man-down, injured officer, or other urgent situation. The emergency alert message may include the video data that served as the basis for the processor1410to issue the emergency alert message.

FIG. 16is a process flow diagram1600of steps executed by a video processing system1400(e.g., through operation of its processor1410) to determine whether a tracked pattern has changed position in a suspicious manner, in accordance with another exemplary embodiment of the present disclosure. Thus, the process flow ofFIG. 16is one exemplary process that may be executed as part of decision block1523ofFIG. 15. The process flow ofFIG. 16is very similar to the process flow ofFIG. 3, except that the process flow ofFIG. 16is primarily focused on detecting when an object, such as a vehicle or person, may be approaching a person under surveillance or a vehicle that transported the person under surveillance to the current location. The process flow illustrated inFIG. 16may have particular applicability for analyzing video data supplied by a camera secured to a rear window, trunk, or roof of a public safety vehicle, such as a police car, fire truck, ambulance, and so forth.

According to the logic flow ofFIG. 16, the processor1410defines (1601) a bounding area for the tracked image pattern. As discussed above with respect toFIG. 3, the bounding area may be defined by a square, rectangle, oval, triangle, or other geometric shape positioned around the tracked image pattern to form a trackable area for purposes of reducing the amount of processing resources necessary to track the image pattern and its positioning over multiple video frames. In other words, each tracked image pattern may be “bounded” within a predefined or adaptive virtual area to make image pattern tracking less processing intensive.

After the processor1410defines a tracked image pattern's bounding area, the processor1410monitors for changes to the tracked pattern bounding area over time (e.g., over a predetermined number of video frames) to determine whether the tracked image pattern changes position in a suspicious manner. The bounding area for a tracked image pattern may shrink, enlarge, move side-to-side and/or angularly, and/or disappear as a tracked image pattern changes position within the camera's video capture area over multiple video frames. Such changes in size and location provide the processor1410with a basis for determining how the tracked image pattern may be changing position over time. For example, the processor1410may determine whether the tracked pattern is moving closer to the camera, moving farther away from the camera, passing through the video capture area, and so forth. From such changes in position, the processor1410may determine whether the tracked image pattern is or has changed position suspiciously so as to warrant alerting the person under surveillance (i.e., the person being protected by the video processing system1400) and/or an emergency management system.

According to the exemplary embodiment ofFIG. 16, monitoring for changes to a tracked image pattern by monitoring for changes to the tracked pattern's bounding area may occur as follows. The processor1410sets (1603) the position of a vehicle containing the camera101or to which the camera101is secured as the reference origin for the video data stream being processed. Thus, the vehicle is the reference point for all calculations and other determinations relevant to evaluating changes of position of a tracked image pattern according to this exemplary embodiment.

Once a reference origin has been set, the processor1410determines (1605) whether the tracked pattern bounding area is becoming progressively larger and/or progressively closer to a bottom of each video frame in the set of subsequent video frames that is subject to image pattern tracking analysis. To determine whether the tracked pattern bounding area is becoming progressively larger in the set of subsequent or otherwise later-in-time video frames, the processor1410may, according to an exemplary embodiment, determine a size of the tracked pattern bounding area in each video frame of the set of subsequent video frames. Based on such bounding area size data, the processor1410may determine a linear regression to model how the size of the tracked pattern bounding area (e.g., size of the pixel area) changes across the set of subsequent video frames. Thereafter, the processor1410may determine a gradient for the linear regression and compare the gradient to a threshold. When the gradient exceeds the threshold, the processor1410may determine that the tracked pattern bounding area is becoming larger over the subsequent video frames. Therefore, according to this exemplary embodiment, the processor1410may be programmed to use a simple or Bayesian linear technique to interpret the bounding area data captured over the set of subsequent video frames for the purpose of evaluating whether the tracked pattern bounding area is becoming progressively larger over time. Those of ordinary skill in the art will readily recognize and appreciate that the processor1410may be programmed to use other known regression or statistical analysis techniques to evaluate how the size of the tracked pattern bounding area is changing over the set of subsequent video frames.

To determine whether the tracked pattern bounding area is becoming progressively closer to a bottom of each video frame in the set of subsequent video frames, the processor1410may, according to an exemplary embodiment, determine a position of a coordinate along a bottom edge of the tracked pattern bounding area in each video frame of the set of subsequent video frames. The determined position may be a pixel position or an estimated physical position of the edge of the boundary area under an assumption that the boundary area actually existed in the real world. For example, the processor1410may determine a position of the center coordinate along the bottom edge of the tracked pattern bounding area, although the position of any coordinate along the bottom edge of the tracked pattern bounding area may suffice with appropriate angular correction applied, if necessary.

The processor1410may then use the bottom coordinate position data to determine a relationship (e.g., an estimated distance) between the position of the coordinate along the bottom edge of the tracked pattern bounding area and the reference origin for each video frame of the set of subsequent video frames. Based on such relationship, the video processing system may determine a linear regression to represent how the relationship between the position of the coordinate along the bottom edge of the tracked pattern bounding area and the reference origin changes across the set of subsequent video frames. For example, the processor1410may determine a distance (e.g., an estimated actual distance or pixel distance) between the position of the coordinate along the bottom edge of the tracked pattern bounding area and the reference origin for each video frame of the set of subsequent video frames and then determine a linear regression to model how the distance changes over time across the set of subsequent video frames.

The processor1410may further determine a gradient for the linear regression and compare the gradient, which may be negative, to a threshold. When the gradient is less than the threshold, the processor110may determine that the tracked pattern bounding area is becoming progressively closer to a bottom of each video frame in the set of subsequent video frames. Those of ordinary skill in the art will readily recognize and appreciate that the processor1410may be programmed to use other known regression or statistical analysis techniques to evaluate how the position of the tracked pattern bounding area is changing over the set of subsequent video frames. Additionally, those of ordinary skill in the art will readily recognize and appreciate that the processor1410may be programmed to use other position coordinates along another edge or edges of the tracked pattern bounding area in order assess whether the tracked pattern bounding area is becoming progressively closer to a bottom of each video frame in the set of subsequent video frames. More detailed exemplary embodiments for using tracked pattern bounding area changes (or lack thereof) over multiple video frames to assist in the determination of whether a tracked pattern has changed position in a suspicious manner are described below with respect toFIGS. 22-25.

When the processor1410determines that the tracked pattern bounding area is becoming progressively larger and/or progressively closer to the bottom of each video frame in the set of subsequent video frames that is subject to pattern tracking analysis, the processor1410determines (1607) that the tracked image pattern has changed position on a suspicious manner. On the other hand, when the processor1410determines that the tracked pattern bounding area is not becoming progressively larger and/or progressively closer to the bottom of each video frame in the set of subsequent video frames that is subject to pattern tracking analysis, the processor1410determines (1609) that the tracked pattern did not change position on a suspicious manner. Thus, according to this embodiment, the processor1410may determine that the tracked image pattern has changed position in a suspicious manner if the tracked pattern bounding area is becoming progressively larger over the set of subsequent video frames, the tracked pattern is becoming progressively closer to the bottom of each frame over the set of subsequent video frames, or both. For example, if the tracked pattern is a pattern of a person, the bounding area is the area of a rectangle positioned around the tracked pattern, and the person is running toward the reference origin (e.g., the vehicle on which the camera101is mounted), the size of the tracked pattern bounding area will progressively increase and a coordinate along the bottom edge of the tracked pattern bounding area will become progressively closer to a bottom of each video frame over the set of subsequent video frames indicating suspicious changes of position of the tracked image pattern. As another example, if the tracked pattern is the pattern of a drone, the bounding area is the area of a rectangle positioned around the tracked pattern, and the drone is flying toward reference origin while also increasing in altitude, the size of the tracked pattern bounding area may not increase over the set of subsequent video frames, but a coordinate along the bottom edge of the tracked pattern bounding area will become progressively closer to a bottom of each video frame over the set of subsequent video frames. In this case, movement of the drone toward the reference origin results in the tracked pattern bounding area becoming progressively closer to a bottom of each frame in the subsequent video frames, thereby indicating a suspicious change of position of the tracked pattern.

FIG. 17is a process flow diagram1700of steps executed by a video processing system1400(e.g., through operation of its processor1410) to determine whether a tracked pattern has changed position in a suspicious manner, in accordance with yet another exemplary embodiment of the present disclosure. The process flow illustrated inFIG. 17is very similar to the process flow illustrated inFIG. 16, except for the primary parameter used for concluding that a tracked image pattern's change in position is suspicious in nature. Thus, the process flow ofFIG. 17is an alternative or additional exemplary process that may be executed as part of decision block1523ofFIG. 15. In contrast to the process flow ofFIG. 16, the process flow ofFIG. 17is primarily focused on detecting when an object, such as a vehicle or person, may be departing an area occupied by a person under surveillance or a vehicle that transported the person under surveillance to the current location. The process flow illustrated inFIG. 17may have particular applicability for analyzing video data supplied by a camera secured to a windshield, rear-view mirror, hood, or roof of a public safety vehicle, such as a police car, fire truck, ambulance, and so forth.

According to the logic flow ofFIG. 17, the processor1410defines (1701) a bounding area for the tracked image pattern. As discussed above with respect toFIGS. 3 and 16, the bounding area may be defined by a square, rectangle, oval, triangle, or other geometric shape positioned around the tracked image pattern to form a trackable area for purposes of reducing the amount of processing resources necessary to track the image pattern and its positioning over multiple video frames.

After the processor1410defines a tracked image pattern's bounding area, the processor1410monitors for changes to the tracked pattern bounding area over time (e.g., over a predetermined number of video frames) to determine whether the tracked image pattern changes position in a suspicious manner. As noted above, the bounding area for a tracked image pattern may shrink, enlarge, move side-to-side and/or angularly, and/or disappear as a tracked image pattern changes position within the camera's video capture area over multiple video frames. Such changes in size and location provide the processor1410with a basis for determining how the tracked image pattern may be changing position over time. For example, the processor1410may determine whether the tracked pattern is getting closer to the camera, moving farther away from the camera, passing through the video capture area, and so forth. From such changes in position, the processor1410may determine whether the tracked image pattern is or has changed position suspiciously so as to warrant alerting the person under surveillance (i.e., the person being protected by the video processing system1400) and/or an emergency management system.

According to the exemplary embodiment ofFIG. 17, monitoring for changes to a tracked image pattern by monitoring for changes to the tracked pattern's bounding area may occur as follows. The processor1410sets (1703) the position of a vehicle containing the camera101or to which the camera101is secured as the reference origin for the video data stream being processed. Thus, the vehicle is the reference point for all calculations and other determinations relevant to evaluating changes of position of a tracked image pattern according to this exemplary embodiment.

Once a reference origin has been set, the processor1410determines (1705) whether the tracked pattern bounding area is becoming progressively smaller and/or progressively further from a bottom of each video frame in the set of subsequent video frames that is subject to image pattern tracking analysis. To determine whether the tracked pattern bounding area is becoming smaller in the set of subsequent or otherwise later-in-time video frames, the processor1410may, according to an exemplary embodiment, determine a size of the tracked pattern bounding area in each video frame of the set of subsequent video frames. Based on such bounding area size data, the processor1410may determine a linear regression to model how the size of the tracked pattern bounding area (e.g., size of the pixel area) changes across the set of subsequent video frames. Thereafter, the processor1410may determine a gradient for the linear regression and compare the gradient to a threshold. When the gradient is less than the threshold, the processor1410may determine that the tracked pattern bounding area is becoming progressively smaller over the subsequent video frames. Therefore, according to this exemplary embodiment, the processor1410may be programmed to use a simple or Bayesian linear technique to interpret the bounding area data captured over the set of subsequent video frames for the purpose of evaluating whether the tracked pattern bounding area is becoming smaller over time. Those of ordinary skill in the art will readily recognize and appreciate that the processor1410may be programmed to use other known regression or statistical analysis techniques to evaluate how the size of the tracked pattern bounding area is changing over the set of subsequent video frames.

To determine whether the tracked pattern bounding area is becoming farther from a bottom of each video frame in the set of subsequent video frames, the processor1410may, according to an exemplary embodiment, determine a position of a coordinate along a bottom edge of the tracked pattern bounding area in each video frame of the set of subsequent video frames. The determined positon may be a pixel position or an estimated physical position of the edge of the boundary area under an assumption that the boundary area actually existed in the real world. For example, the processor1410may determine a position of the center coordinate along the bottom edge of the tracked pattern bounding area, although the positon of any coordinate along the bottom edge of the tracked pattern bounding area may suffice with appropriate angular correction applied, if necessary.

The processor1410may then use the bottom coordinate position data to determine a relationship (e.g., an estimated distance) between the position of the coordinate along the bottom edge of the tracked pattern bounding area and the reference origin for each video frame of the set of subsequent video frames. Based on such relationship, the video processing system may determine a linear regression to represent how the relationship between the position of the coordinate along the bottom edge of the tracked pattern bounding area and the reference origin changes across the set of subsequent video frames. For example, the processor1410may determine a distance (e.g., an estimated actual distance or pixel distance) between the position of the coordinate along the bottom edge of the tracked pattern bounding area and the reference origin for each video frame of the set of subsequent video frames and then determine a linear regression to model how the distance changes over time across the set of subsequent video frames.

The processor1410may further determine a gradient for the linear regression and compare the gradient, which may be negative, to a threshold. When the gradient is greater than the threshold, the processor110may determine that the tracked pattern bounding area is becoming progressively further from a bottom of each video frame in the set of subsequent video frames. Those of ordinary skill in the art will readily recognize and appreciate that the processor1410may be programmed to use other known regression or statistical analysis techniques to evaluate how the position of the tracked pattern bounding area is changing over the set of subsequent video frames. Additionally, those of ordinary skill in the art will readily recognize and appreciate that the processor1410may be programmed to use other position coordinates along another edge or edges of the tracked pattern bounding area in order assess whether the tracked pattern bounding area is becoming further from a bottom of each video frame in the set of subsequent video frames. More detailed exemplary embodiments for using tracked pattern bounding area changes (or lack thereof) over multiple video frames to assist in the determination of whether a tracked pattern has changed position in a suspicious manner are described below with respect toFIGS. 22-25.

When the processor1410determines that the tracked pattern bounding area is becoming progressively smaller and/or progressively further from the bottom of each video frame in the set of subsequent video frames that is subject to pattern tracking analysis, the processor1410determines (1707) that the tracked image pattern has changed position on a suspicious manner. On the other hand, when the processor1410determines that the tracked pattern bounding area is not becoming progressively smaller and/or progressively further or farther from the bottom of each video frame in the set of subsequent video frames that is subject to pattern tracking analysis, the processor1410determines (1709) that the tracked pattern has not changed position in a suspicious manner. Thus, according to this embodiment, the processor1410may determine that the tracked image pattern has changed position in a suspicious manner if the tracked pattern bounding area is becoming progressively smaller over the set of subsequent video frames, the tracked pattern is becoming progressively further from the bottom of each frame over the set of subsequent video frames, or both. For example, if the tracked pattern is a pattern of a person, the bounding area is the area of a rectangle positioned around the tracked pattern, and the person is running away from the reference origin (e.g., the vehicle on which the camera101is mounted), the size of the tracked pattern bounding area will decrease and a coordinate along the bottom edge of the tracked pattern bounding area will become further from a bottom of each video frame over the set of subsequent video frames indicating suspicious changes of position of the tracked image pattern (e.g., indicate that the person is running away from a police car to which the camera101is mounted).

FIG. 18illustrates an exemplary use case for the processes and system ofFIGS. 14-17. The illustrated use case depicts a car1801pulled over to the side of a roadway1805with a police car1803parked or running idle directly behind the car1801. For example, the police car1803may have pulled the car1801over to the side of the roadway1805for a traffic violation or for some other reason. The depicted use case shows other cars passing by the pulled-over car1801and the police car1803as the other cars traverse the roadway1805. The depicted use case further shows another car1812approaching the police car1803from the rear. The approaching car1812and its occupants may pose a threat to the officer driving the police car1803.

The police car1803may include one or more video cameras1807-1809integrated with or mounted to parts of the police car1803. For example, the police car1803may include a forward-directed camera1807, a multi-directional camera1808, and/or a rearward-directed camera1809. The forward-directed camera1807may be mounted to the windshield or the hood of the car1803, or may be mounted to or incorporated into a camera system that incorporates the car's rear-view mirror1810. An exemplary, uniquely-constructed camera system that includes a rear-view mirror assembly and a video camera, as well as an exemplary software process for processing video data captured by the camera, are described in more detail below with respect toFIGS. 19-22. The multi-directional camera1808may be mounted to a roof of the car1803and provide video capture in the forward and rearward directions. For example, the multi-directional camera system1808may include a panoramic video camera having an optical axis perpendicular to the roof of the car1803such that the camera captures video in a field of view of 360° horizontal by at least 180° vertical. The rearward-directed camera1809may be mounted to the rear window or trunk of the car1803. One of skill in the art will readily recognize and appreciate that the police car1803may include one more cameras mounted at other locations thereof in addition to or instead of the cameras1807-1809depicted inFIG. 18.

According to one exemplary embodiment, each camera1807-1809includes a lens or lens system, at least one image sensor positioned in light-sensing relation to the lens/lens system, a video processor, a central processor (which may incorporate the video processor), appropriate operational software, and other conventional components necessary to capture video in the applicable direction for the particular camera1807-1809. Each camera1807-1809may also include wireless communication capability to enable the camera's central or video processor to send raw or processed video data to a remote video processing system, communicate alerts to mobile devices executing a complementary application, and/or communicate alerts and/or video data to a remote emergency management system. Each camera1807-1809may further include a variety of sensors (e.g., an accelerometer, gyroscope, inertial measurement unit, magnetometer, GPS, etc.) providing outputs to the central or video processor to enable the processor to detect various inertial and locational changes affecting the camera1807-1809and/or the police car1803incorporating it. Where the camera1807-1809performs video analysis locally, the camera's software and hardware may be configured to perform any of the processes described above with respect toFIGS. 2-4, 6, 7, 9-11, and 15-17. The camera's software and hardware may also be configured to perform any of the processes described below with respect toFIGS. 21-26.

FIG. 19illustrates a top view of a video camera system1900in accordance with one exemplary embodiment of the present disclosure. The video camera system1900may be used to implement a windshield-attachable camera, such as the forward-directed camera1807in the stopped-vehicle use case ofFIG. 18. The camera system1900includes a rear-view mirror assembly and a video camera1905. The rear-view mirror assembly includes an adjustable mirror subassembly1901pivotally connected to a rigid arm1903. The mirror subassembly1901includes a rear surface1907and a front-facing, generally oblong mirror1909. The mirror subassembly1901defines a longitudinal axis1911that passes perpendicularly through a center of the mirror1909. The rigid arm1903is attachable to a windshield1913of a motor vehicle (e.g., police car1803).

The video camera1905includes, inter alia, a lens1915, which may be a multi-lens system as well understood in the art. The lens1915defines horizontal and vertical fields of view in which images are capturable by the video camera1905. Each of the horizontal field of view and the vertical field of view may be 150° or greater depending upon the configuration of the selected lens1915.

The video camera1905may be secured to or form part of the rear surface1907of the mirror subassembly1901. In the embodiment depicted inFIG. 19, the video camera1905is integrated into the mirror subassembly1901with the camera's lens1915projecting outward from the rear surface1907of the mirror subassembly1901at a position closer to an expected location or position of an operator of the motor vehicle into which the video camera system1900will be installed. The lens1915of the video camera1905is positioned such that an optical axis1919of the lens1915is fixedly oriented at an angle1921in a range of about 5° to about 11° toward the expected position of the operator of the motor vehicle (e.g., toward the driver side of the vehicle) relative to an axis1917parallel to the longitudinal axis1911of the mirror subassembly1901. The optical axis1919of the lens1915may be further fixedly oriented at an angle in a range of about 9° to about 21° toward an expected position of a roof of the motor vehicle relative to the axis1917parallel to the longitudinal axis1911of the mirror subassembly1901(see, for example, angle2033inFIG. 20). Thus, the optical axis1919of the video camera1905is pre-oriented during fabrication of the mirror subassembly1901or during attachment of the video camera1905to the mirror subassembly1901so as to be offset toward what would be the driver side of the vehicle (left or right depending upon the country) and/or toward what would be the roof of the vehicle (e.g., upward) to account for the positioning of the video camera1905along the rear surface1907of the mirror subassembly1901and optionally to account for a typical orientation of the mirror subassembly1901by an average-size vehicle operator.

In an alternative embodiment, the optical axis1919of the lens1915may be electronically oriented or steered such that a target capture area within the horizontal and vertical fields of view of the lens1915is centered at an angle in the range of about 5° to about 11° toward the expected position of the operator of the motor vehicle relative to an axis1917parallel to the longitudinal axis1911of the mirror subassembly1901. Similarly, the optical axis1919of the lens1915may be further electronically oriented or steered such that a target capture area within the horizontal and vertical fields of view of the lens1915is also centered at an angle in a range of about 9° to about 21° toward an expected position of a roof of the motor vehicle relative to the axis1917parallel to the longitudinal axis1911of the mirror subassembly1901. The process for performing electronic steering of the lens' optical axis1917may be similar to the process described below with respect toFIGS. 21 and 22, where the angular differences (angles) used in such process are fixed in the angular ranges set forth above and the reference longitudinal axis used in such process is the axis1917parallel to the longitudinal axis1911of the mirror subassembly1901.

According to one embodiment, the video camera1905may be positioned on or along the rear surface1907of the mirror subassembly1901closer to the expected position of an operator of the motor vehicle than to an expected position of a passenger of the motor vehicle. Alternatively, the video camera1905may be positioned on the rear surface1907of the mirror subassembly1901closer to the expected position of a passenger of the motor vehicle than to an expected position of an operator of the motor vehicle. The angle1921of optical axis pre-orientation takes into account the position of the video camera1905on the rear surface1907of the mirror subassembly1901, which may include any curvature of the rear surface1907of the mirror subassembly1901affecting such position. By pre-orienting the optical axis1919of the video camera's lens1915during manufacture of the video camera system1900, the video camera1905is more likely to capture images directly in front of the windshield1913during use of the mirror subassembly1901by the vehicle's operator.

The exemplary video camera system1900illustrated inFIG. 19may be considered to form all or part of a single camera version of the video processing system1400as generally described above with respect toFIGS. 14-17. Thus, the video camera system1900may include, inter alia, video capture, audio capture, motion-sensing, video and/or audio processing, communications, and alerting functionality as was described above with respect to the video processing system1400ofFIG. 14. Therefore, for purposes of describing the exemplary use case ofFIG. 18and the exemplary video camera systems1900and2000ofFIGS. 19 and 20, reference will be made to the electrical blocks depicted inFIG. 14as though such blocks form parts of the video cameras1807-1809ofFIG. 18and/or the video camera systems1900,2000ofFIGS. 19 and 20. The electrical and other components of the video processing system1400may be incorporated into the video cameras1807-1809ofFIG. 18and/or the camera1905or the mirror subassembly1901of the video camera system1900ofFIG. 19.

FIG. 20illustrates a side view of an alternative video camera system2000in accordance with another exemplary embodiment of the present disclosure. The video camera system2000illustrated inFIG. 20is substantially identical to the video camera system1900illustrated inFIG. 19, except that the video camera2005is positioned on or along the rear surface2007of the mirror subassembly2001closer to the expected position of a passenger of the motor vehicle than to an expected position of an operator of the motor vehicle.

Similar to video camera system1900, video camera system2000may be used to implement a windshield-attachable camera, such as the forward-directed camera1807in the stopped-vehicle use case ofFIG. 18. The camera system2000includes a rear-view mirror assembly and a video camera2005. The rear-view mirror assembly includes an adjustable mirror subassembly2001pivotally connected to a rigid arm2003. The mirror subassembly2001includes a rear surface2007and a front-facing, generally oblong mirror2009. The mirror subassembly2001defines a longitudinal axis2011that passes perpendicularly through a center of the mirror2009. The rigid arm2003is attachable to a windshield2013of a motor vehicle (e.g., police car1803).

The video camera2005includes, inter alia, a lens2015, which may be a multi-lens system as well understood in the art. The lens2015defines horizontal and vertical fields of view in which images are capturable by the video camera2005. Each of the horizontal field of view and the vertical field of view may be 150° or greater depending upon the configuration of the selected lens2015.

The video camera2005may be secured to or form part of the rear surface2007of the mirror subassembly2001. In the embodiment depicted inFIG. 20, the video camera1905is integrated into the mirror subassembly2001with the camera's lens2015projecting outward from the rear surface2007of the mirror subassembly2001at a position closer to an expected location or position of a passenger of the motor vehicle into which the video camera system1900will be installed. The lens2015of the video camera2005is positioned such that an optical axis2019of the lens2015is fixedly oriented at an angle in a range of about 5° to about 11° toward the expected position of the operator of the motor vehicle relative to an axis parallel to the longitudinal axis of the mirror subassembly2001(e.g., such as illustrated inFIG. 19and described above with regard to optical axis1919, angle1921, and axis1917). The optical axis2019of the lens2015may be further fixedly oriented at an angle2033in a range of about 9° to about 21° toward an expected position of a roof2014of the motor vehicle relative to an axis2017parallel to the longitudinal axis2011of the mirror subassembly2001. Thus, the optical axis2019of the video camera2005is pre-oriented during fabrication of the mirror subassembly2001or during attachment of the video camera2005to the mirror subassembly2001so as to be offset toward what would be the driver side of the vehicle (left or right depending upon the country) and/or toward what would be the roof2014of the vehicle (e.g., upward) to account for the positioning of the video camera2005along the rear surface2007of the mirror subassembly2001and optionally to account for a typical orientation of the mirror subassembly2001by an average-size vehicle operator. The angle2033of optical axis pre-orientation takes into account the position of the video camera2005on the rear surface2007of the mirror subassembly2001, which may include a distance1925between the longitudinal axis1911of the mirror subassembly1901and a parallel axis1917passing through a center of the camera lens1915, as well as any curvature of the rear surface2007of the mirror subassembly1901affecting the camera's position. By pre-orienting the optical axis2019of the video camera's lens2015during manufacture of the video camera system2000, the video camera2005is more likely to capture images directly in front of the windshield2013during use of the mirror subassembly2001by the vehicle's operator.

In an alternative embodiment, the optical axis2019of the lens2015may be electronically oriented or steered such that a target capture area within the horizontal and vertical fields of view of the lens2015is centered at an angle in the range of about 5° to about 11° toward the expected position of the operator of the motor vehicle relative to an axis parallel to the longitudinal axis of the mirror subassembly2001(e.g., such as illustrated inFIG. 19and described above with regard to optical axis1919, angle1921, and axis1917). Similarly, the optical axis2019of the lens2015may be further electronically oriented or steered such that a target capture area within the horizontal and vertical fields of view of the lens2015is also centered at an angle in a range of about 9° to about 21° toward an expected position of a roof2014of the motor vehicle relative to an axis2017parallel to the longitudinal axis2011of the mirror subassembly2001. The process for performing electronic steering of the lens' optical axis2019may be similar to the process described below with respect toFIGS. 21 and 22, where the angular differences (angles) used in such process are fixed in the angular ranges set forth above and the reference longitudinal axis used in such process is the axis2017parallel to the longitudinal axis2011of the mirror subassembly2001.

Similar to exemplary video camera system1900, exemplary video camera system2000may also be considered to form all or part of a single camera version of the video processing system1400as generally described above with respect toFIGS. 14-17. Thus, the video camera system2000may include, inter alia, video capture, audio capture, motion-sensing, video and/or audio processing, communications, and alerting functionality as was described above with respect to the video processing system1400ofFIG. 14. The video camera systems1900,2000ofFIGS. 19 and 20are sufficiently similar that considering them and their respective views together permits a more comprehensive understanding of how either video camera system1900,2000may operate to capture images in front of the vehicle through the windshield1913,2013. Thus, both systems1900,2000will be referenced in connection with describing the exemplary video data extraction process flow diagram2100ofFIG. 21. The process illustrated inFIG. 21may be executed by a processor1410of either system1900,2000.

Because the mirror subassembly1901,2001may be pivotally or rotatably moved by an operator of the vehicle in which it is used, the video capture area of the camera1905,2005may likewise move and ultimately capture unwanted images, such as an image of the sky or an image of the hood of the vehicle, instead of or in addition to desired images in front of the vehicle. Thus, in order to increase the likelihood that processed video data includes the most relevant video data (e.g., video data that could include image patterns worthy of tracking), the processor1410may execute the logic flow process ofFIG. 21to select a subset of the video data captured by the camera1905,2005for further processing. The selected subset of video data corresponds to a target capture area within the horizontal and vertical fields of view of the video camera's lens1915,2015, which corresponds to an area of the windshield1913,2013from which image pattern monitoring is desired. According to one embodiment, the horizontal and vertical fields of view of the video camera's lens1915,2015are at least 10° greater than horizontal and vertical angular dimensions of the target capture area.

According to the logic flow ofFIG. 21, the processor1410receives (2101) video data from the video camera1905,2005. The video data represents images captured in the horizontal and vertical fields of view of the camera lens1915,2015, as may be limited by the capabilities of the selected image sensor(s). The processor1410also receives (2103) sensor data from a motion-sensing subsystem1401of the video camera system1901,2001. The motion-sensing subsystem1401may be integrated into the mirror subassembly1901,2001or elsewhere within or on the vehicle and communicates its sensor data to the processor1410. The motion-sensing subsystem1401may include multiple sensors that supply varying types of sensor data to the processor1410. The types of sensor data that may be supplied include velocity (speed and direction), roll, pitch, yaw, and location. The sensor data may be supplied periodically, upon request from the processor1410, or otherwise.

After receiving the sensor data, the processor1410determines (2105) a reference longitudinal axis and an orientation of the camera lens' optical axis based on such data. For example, the processor1410may determine the reference longitudinal axis as the direction in which the vehicle (and the video camera system1901,2001) is currently traveling based on the output of an inertial measurement unit (IMU) or other motion sensors within the motion-sensing subsystem1401. The processor1410may also determine a current orientation of the camera lens' optical axis by adjusting a factory present orientation by a change in orientation as detected by the IMU or other motion sensors within the motion-sensing subsystem1401. As described above, the camera1905,2005and its lens1915,2015may be configured during manufacture of the rear-view mirror assembly such that the lens' optical axis is angled in two or more planes relative to an expected position of the vehicle operator and optionally the expected position of vehicle's roof2014to account for, inter alia, the camera's position on or along the rear surface1907,2007of the mirror subassembly1901,2001. Therefore, absent sensor data indicating a change in orientation of the mirror subassembly1901,2001, the processor1410is programmed to determine video data for a target capture area within the video data received from the video camera1905,2005, where the target capture area is, for example, in front of the vehicle, centered on the reference longitudinal axis, and substantially parallel to the horizon.

When the mirror subassembly is moved by an operator of the vehicle, the location of the target capture area within the horizontal and vertical fields of view of the video camera lens1915,2015will change if not appropriately compensated. Thus, the processor1410must determine how the target capture area has moved within the video data received from the camera1905,2005so as to maintain the target capture area for which video data is utilized as being centered on the reference longitudinal axis and substantially parallel to the horizon. The processor1410will then use the new video data from the target capture area to perform image pattern analysis and various other processes as described throughout this specification.

Where the motion-sensing subsystem1401has communicated sensor data to the processor indicating that the mirror subassembly1901,2001has been moved from its factory pre-set position, the processor1410determines (2107) angular differences or changes between the orientation of the camera lens' optical axis after the movement and the reference longitudinal axis. Depending how the mirror subassembly1901,2001has been moved, the angular differences may be in two or more planes. For example, as illustrated inFIGS. 19 and 20, movement of the mirror subassembly1901,2001may result in changes in the position of the camera lens' optical axis by angles1923,2037in one or more planes relative to the reference longitudinal axis, which may be the same as the longitudinal axis2011of the mirror subassembly2001under certain circumstances. Such movement of the mirror subassembly1901,2001may cause the longitudinal axis1911,2011of the mirror subassembly1901,2001to move angularly to new positions1927,2035as detected by the motion-sensing subsystem1401.

After the processor1410determines the angular changes made to the camera lens' optical axis as a result of movement of the mirror subassembly1901,2001, the processor1410determines (2109) a location of the target capture area within the horizontal and vertical fields of view of the camera lens1915,2015based on such angular differences/changes. For example, the processor1410may determine the post-movement target capture area as the moved target capture area rotated by angles equal and opposite to the angular differences caused by the movement of the mirror subassembly1901,2001. After the target capture area has been determined, the processor1410selects (2111) a portion of the received video data corresponding to the video data in the post-movement target capture area and then uses the selected video data for all further processing, including image pattern tracking and suspicious activity detection. In other words, upon electronically returning the target capture area post-movement to its pre-movement location, the video data corresponding to the post-movement target capture area will correspond to a different set of pixels of the camera's image sensor than the video data corresponding to the pre-movement target capture area. While the process ofFIG. 21was described above with respect to movement of the mirror subassembly1901,2001, the describe process is equally applicable to account for movement of the camera1905,2005alone, where the camera1905,2005may be movable without necessarily moving the mirror subassembly1901,2001.

To provide an example of how the process flow ofFIG. 21may be used to electronically maintain the target capture area as being generally centered on a reference longitudinal axis (e.g., as may be determined by the direction of movement of the video camera system and/or vehicle) and substantially parallel to the horizon, reference is made toFIG. 22. As shown in the top illustration of the figure, a target capture area2204is approximately centered on a reference longitudinal axis2206(which, in this case, also corresponds to the camera lens' optical axis1919,2019) and within the horizontal and vertical fields of view2202,2203of the camera's lens1915,2015. The top illustration represents the general location of the target capture area2204when the video camera system1900,2000is initially installed in the vehicle. As discussed above, the camera's lens1915,2015may be physically constructed such that the lens' optical axis1919,2019is angled within a particular range of angles toward a driver position of the vehicle and/or toward a roof of the vehicle so as to generally center the target capture area2204about the reference longitudinal axis2206and position the target capture area2204substantially parallel to the horizon2218(e.g., within +/−10 degrees of the horizon2218). According to one embodiment, the target capture area2204may initially reside within the horizontal and vertical fields of view2202,2203of the video camera's lens1915,2015such that the horizontal and vertical fields of view2202,2203are at least 10° greater than the horizontal and vertical angular dimensions of the target capture area2204.

From a more technical standpoint, the horizontal and vertical fields of view2202,2203of the camera lens1915,2015generally define the area through which light will pass onto an image sensor positioned in light-receiving relation to the lens1915,2015. Thus, the image sensor of the video camera1905,2005detects images present at pixel positions within the entire field of view of the camera1905,2005(i.e., the area defined by the horizontal and vertical fields of view2202,2203). However, for purposes of the process shown inFIG. 21, a target capture area2204is limited to a subset of the overall field of view of the camera lens1915,2015to enable the processor1410to maintain the target capture area substantially in its original position (albeit with a different set of pixel positions on the image sensor) when the optical axis1919,2019of the camera lens1915,2015moves together with movement of either the mirror subassembly1901,2001of the rear-view mirror assembly or the video camera1905,2005alone.

When the operator of the vehicle moves the mirror subassembly1901,2001of the rear-view mirror assembly so as to position the mirror1909,2009in a desired position for viewing traffic behind the vehicle (or alternatively moves the video camera1905,2005alone (when so movable)), the target capture area2204moves together with the optical axis1919,2019and the horizontal and vertical fields of view2202,2203of the camera's lens1915,2015as illustrated in an exemplary manner in the bottom illustration ofFIG. 22. In such a case and absent processor correction, the target capture area2204moves so as to remain centered about the camera lens' optical axis1919,2019, but is no longer centered about the reference longitudinal axis2206and may no longer be parallel to the horizon2218. Thus, if the target capture area remains uncorrected, the target capture area may not include a desired view of traffic in front of the vehicle and may include images of the vehicle's hood or other undesirable objects.

In accordance with the process ofFIG. 21, movement of the mirror subassembly1901,2001and/or the camera1905,2005is detected by the motion-sensing subsystem1401, and sensor data supplied by the motion-sensing subsystem1401is used by the processor1410to reset the target capture area to its original orientation substantially centered about the reference longitudinal axis2206and substantially parallel to the horizon2218. Thus, after execution of the process ofFIG. 21, the corrected/maintained target capture area2214has the same orientation and position as the original, pre-movement target capture area2204shown in the top illustration ofFIG. 22. However, due to the movement of the camera lens' optical axis1919,2019, the location of target capture area2214on the camera's image sensor encompasses a different set of pixel positions than did the original, pre-movement target capture area2204. To determine which area of pixels on the image sensor represent target capture area2204subsequent to movement of the mirror subassembly1901,2001or the video camera1905,2005, the processor1410uses the sensor data received from the motion-sensing subsystem1401to determine angular differences or changes between the orientation of the camera lens' optical axis after the movement and the reference longitudinal axis2206. In other words, the processor1410uses the sensor data to determine how the optical axis of the camera lens1915,2015has moved relative to the reference longitudinal axis2206. By knowing how the optical axis of the lens1915,2015has repositioned, the processor1410can determine how the target capture area2204, which is centered about the optical axis, has also repositioned due to the movement of the mirror subassembly1901,2001or the video camera1905,2005. Having made such a determination, the processor1410electronically undoes the repositioning of the target capture area2204by selecting the portion of the received video data corresponding to a pixel area representing the target capture area2214at its original position.

As shown in the bottom illustration ofFIG. 22and assuming that the image sensor of the camera1905,2005captures images within the area defined by the horizontal and vertical fields of view2202,2203of the lens1915,2015, the maintained target capture area2214substantially replicates the area size and orientation of the pre-movement target capture area2204shown in the top illustration ofFIG. 22. However, the maintained target capture area2214encompasses a different set of image sensor pixels than does the pre-movement target capture area2204, although there would likely be some overlap as illustrated in exemplary fashion in the bottom illustration ofFIG. 22. The video data from the maintained target capture area2214is then used by the processor1410to perform other functions, such as image pattern tracking and suspicious activity detection.

Referring back to the motor vehicle use case ofFIG. 18, suspicious activity detection may be performed by the video processing system1400through receipt and analysis of video data from one or more of the exemplary video cameras1807-1809. For example,FIG. 23provides one exemplary illustration for how the system processor1410may analyze a set of received video frames to perform suspicious activity detection and tracking. According to this embodiment, the processor1410receives streaming video data from a camera (e.g., forward-directed camera1807) and extracts therefrom data representing a video frame2301(e.g., Video Frame N inFIG. 23). The processor1410compares the video frame data to data representing a set of one or more predefined patterns stored in memory114(which may be local memory or remote memory). In the illustrated case, the set of predefined patterns includes one or more patterns for an automobile or other vehicle. Automobile patterns may include patterns for various portions or components of the automobile such as, for example, the roof, windshield, rear window, side window, side door, hood, trunk, front bumper, rear bumper, license plate(s), tires, headlights, rear lights, and so forth, as well as composite patterns that may include one of more individual automobile components (e.g., an automobile composite rear pattern that combines patterns for the roof, rear window, trunk, rear bumper, tail lights (and other rear lights), license plate, side view mirrors, rear tires, and other identifiable components from the perspective of standing behind the automobile and looking toward it). In the example illustrated inFIG. 23, the processor1410determines that the outline of the rear of a car2314(which could be car1801fromFIG. 18) is substantially similar to a stored predefined pattern, such as a stored automobile composite rear pattern. In response to such determination, the processor1410may define a bounding area2306for the detected pattern2314by bounding the pattern2314with a simpler geometric shape (e.g., a rectangle in this particular case). According to one exemplary embodiment, the processor1410may commence pattern tracking upon detecting the predefined pattern2314within the video frame2301and then defining a tracked pattern bounding area2306for the pattern2314. According to an alternative embodiment having substantially greater processing resources, the automobile composite rear pattern2314may be tracked directly without using the easier-to-process bounding area2306.

According to the embodiment illustrated inFIG. 23, the processor1410may set the position of the vehicle (e.g., police car1803) as the reference origin for images captured by the forward-directed camera1807(or the multi-directional camera1808), if the processor1410hasn't already done so when determining whether to commence pattern tracking. Setting the position of the police car1803as the reference origin provides a point of view for the processor1410to assess suspicious activity that could affect the police officer operating the car1803, who is the person under surveillance for this example. To evaluate potential suspicious activity, the processor1410may monitor the size of the tracked pattern bounding area2306over a set of video frames2302,2303that are subsequent to or otherwise later in time than the video frame2301that resulted in commencement of pattern tracking in the first place (two video frames2302,2303are shown in the set of subsequent video frames for illustration, but the set may include ten or more video frames as described above). The set of subsequent/later-in-time video frames2302,2303over which a tracked pattern2314or its bounding area2306is analyzed may be sequential in nature (e.g., using the nomenclature fromFIG. 23, MA may equal “1,” MBmay equal “2,” and so forth) or may be otherwise selected over the tracking time period (e.g., MA may equal “5”, MBmay equal “10,” and so forth based on how the video frames to be analyzed are selected). The video frames2301-2303may include video data representing the entire field of view of the applicable camera1807(i.e., within the area defined by the camera lens' horizontal and vertical fields of view) or may only include video data representing a target capture area2204within the overall field of view of the camera1807. Use of a target capture area2204may be applicable when the camera1807is part of a video camera system1900,2000, such as those described above with respect toFIGS. 19-22.

When the size of the tracked pattern bounding area2306becomes progressively smaller over the set of subsequent video frames2302,2303(e.g., as illustrated inFIG. 23), the processor1410may determine that the tracked pattern2314is fleeing the scene and, therefore, has changed position in a suspicious manner. To determine whether the tracked pattern bounding area2306is becoming smaller over several video frames, the processor1410may use statistical processing to analyze the measured bounding area sizes. For example, the processor1410may determine a linear regression from the bounding area size data to represent how the size of the tracked pattern bounding area2306changes across the set of subsequent video frames2302,2303. The processor1410may then determine a gradient for the linear regression and compare the gradient to a threshold. For example, in the context of a car1801leaving the scene of a traffic stop, the gradient threshold may be set in the range of −0.10 and −0.20, which equates to a 10.0% to 20.0% decrease in bounding area size per second. When the gradient is less than its threshold (a negative number in this case), the processor1410determines that the tracked pattern bounding area2306is becoming smaller over the set of subsequent video frames2302,2303.

Additionally or alternatively, the processor1410may be programmed to determine whether the tracked pattern bounding area2306is becoming progressively farther from a bottom of each frame2302,2303in the subsequent set of video frames2302,2303. For example, where the police car1803is set as the reference origin for images captured by the forward-directed camera1807(i.e., where the camera1807provides a point of view from the front of the police car1803), movement of the tracked pattern2314toward the top of each video frame over multiple video frames indicates that the tracked pattern2314may be fleeing the scene and, therefore, has changed position in a suspicious manner. According to this embodiment, the processor1410determines a position of a coordinate2308along a bottom edge of the tracked pattern bounding area2306and a relationship between the position of the coordinate2308along the bottom edge of the tracked pattern bounding area2306and the reference origin for each video frame2301-2303being analyzed. In the example illustrated inFIG. 23, the relationship between the position of the coordinate2308along the bottom edge of the tracked pattern bounding area2306and the reference origin is a distance2312(e.g., pixel distance) between the coordinate2308along the bottom edge of the tracked pattern bounding area2306and a coordinate2310along a bottom edge of the video frame2301-2303(or some other defined area within the frame2301-2303) as defined by the dimensions of the video frame2301-2303. The coordinate2308on the bottom edge of the tracked pattern bounding area2306may be approximately centered along the bottom edge of the tracked pattern bounding area2306and the coordinate2310on the bottom edge of the frame2301may be likewise centered along the bottom edge of the frame2301as illustrated in frame2301. However, as illustrated in the other two frames2302,2303, the coordinates2308,2310along the bottom edges of the tracked pattern bounding area2306and the frame2302,2303may be off-center. In the exemplary scenario depicted inFIG. 23, the coordinate2308on the bottom edge of the tracked pattern bounding area2306remains centered along the bottom edge of the tracked pattern bounding area2306, but the coordinate2310on the bottom edge of the frame2302,2303moves to the left over time to permit a simple determination of the distance2312between the two coordinates2308,2310, such as may be the case if the stopped car1801fled the scene and attempted to merge back onto the roadway1805.

To determine whether the tracked pattern bounding area2306is becoming progressively farther from the bottom of the frames over the analyzed, later-in-time video frames2302,2303, the processor1410may use statistical processing to analyze the change in relationship (e.g., distance) between the tracked pattern bounding area2306and the bottom of each frame2302,2303. For example, the processor1410may determine a linear regression from the bounding area edge-to-frame edge distance data to represent how the relationship between the position of the coordinate2308along the bottom edge of the tracked pattern bounding area2306and the position of the coordinate2310along the bottom edge of the frame2302,2303changes across the set of subsequent video frames2302,2303. The processor1410may then determine a gradient for the linear regression and compare the gradient to a threshold. For example, in the context of a stopped car leaving a traffic stop prematurely, the gradient threshold may be set in the range of 0.10 and 0.15, which equates to a 10% to 15% increase in distance per second. When the gradient is greater than its threshold, the processor1410may determine that the tracked pattern bounding area2306is becoming farther from the bottom of each frame2302,2303(and, therefore, farther from the reference origin, such as the front of the police car1803) over the set of subsequent video frames2302,2303. The processor1410may analyze bounding area size changes, bounding area positioning relative to a reference origin or other reference point, both bounding area size changes and bounding area positioning, and/or any other video data-based characteristics to make its final determination as to whether a tracked pattern has changed position in a suspicious manner.

In addition to detecting and analyzing an automobile composite rear pattern2314for purposes of determining whether a stopped car1801is attempting to flee the scene of a traffic stop, the processor1410may detect and analyze individual component patterns within the composite pattern2314. For example, the processor1410may compare video frame data to data representing a license plate pattern stored in memory114. For example, the processor1410may compare the various components of the automobile composite rear pattern2314to isolate a license plate2320. Where such a license plate pattern is detected, the processor1410may communicate an image of the license plate to a motor vehicle department computer system for further analysis.

FIG. 24provides another exemplary illustration for how the system processor1410may analyze a set of received video frames to perform suspicious activity detection and tracking in connection with the traffic stop use case ofFIG. 18. More particularly, the embodiment shown inFIG. 24illustrates how the processor1410may utilize pattern tracking to detect a man-down (or officer-down) situation during a traffic stop or otherwise. According to this embodiment, the processor1410receives streaming video data from a camera1807-1809and extracts therefrom data representing a video frame2401(e.g., Video Frame N inFIG. 24). The processor1410compares the video frame data to data representing a set of one or more predefined patterns stored in memory114(which may be local memory or remote memory). In the illustrated case, the set of predefined patterns may include one or more patterns for features of a police officer in general, for features of a person in general, and/or for features of the actual person under surveillance (i.e., the police officer at the scene). In the example illustrated inFIG. 24, the processor1410determines that the outline of a person2414resembling the officer under surveillance is substantially similar to a stored predefined pattern. In response to such determination, the processor1410may define a bounding area2406for the detected pattern2414by bounding the pattern2414with a simpler geometric shape (e.g., a rectangle in this particular case). According to one exemplary embodiment, the processor1410may commence pattern tracking upon detecting the predefined pattern2414within the video frame2401and then defining a tracked pattern bounding area2406for the pattern2414. According to an alternative embodiment having substantially greater processing resources, the officer pattern2414may be tracked directly without using the easier-to-process bounding area2406.

To evaluate potential suspicious activity (e.g., a man down), the processor1410may monitor a variety of parameters or features of the tracked pattern bounding area2406over a set of video frames2402-2404that are subsequent to or otherwise later in time than the video frame2401that resulted in commencement of pattern tracking in the first place (three video frames2402-2404are shown in the set of subsequent video frames for illustration, but the set may include ten or more video frames as described above). The set of subsequent/later-in-time video frames2402-2404over which a tracked pattern2414or its bounding area2406is analyzed may be sequential in nature (e.g., using the nomenclature fromFIG. 24, Mxmay equal “1,” Mymay equal “2,” Mzmay equal “3,” and so forth) or may be otherwise selected over the tracking time period (e.g., Mxmay equal “5”, Mymay equal “10,” Mzmay equal “15,” and so forth based on how the video frames to be analyzed are selected). The video frames2401-2404may include video data representing the entire field of view of the applicable camera1807-1809(i.e., within the area defined by the camera lens' horizontal and vertical fields of view) or may only include video data representing a target capture area2204within the overall field of view of the camera1807-1809. Use of a target capture area2204may be applicable when the camera1807is part of a video camera system1900,2000, such as those described above with respect toFIGS. 19-22.

According to this exemplary embodiment, one feature of the tracked pattern bounding area2406that may be monitored during the later-in-time video frames2402-2404is movement of the tracked pattern bounding area2406, and the speed thereof, over time relative to the ground or a bottom of the frame2402-2404. The monitoring of such movement and speed may enable the processor1410to determine whether a man-down condition exists. For example, the processor1410may be programmed to determine whether the tracked pattern bounding area2406has moved downward rapidly over a sequence of video frames representing a predetermined time period (e.g., five seconds or less). If the processor1410detects such a rapid downward movement, the processor1410may determine that the tracked pattern2414has changed position in a suspicious manner and may communicate an emergency message relating to a man-down condition to an emergency management system operated by law enforcement, for example.

According to one embodiment, the processor1410may estimate downward movement of the of the tracked pattern bounding area2406by determining whether the tracked pattern bounding area2406is becoming rapidly closer to a bottom of each video frame2402,2403of a set of video frames2402,2403analyzed over the predetermined time period and/or whether the tracked pattern bounding area2406has moved so far downward that it is no longer in the video frame, such as shown in frame2404. For example, movement of the tracked pattern2414toward and/or past the bottom of each video frame over multiple video frames indicates that the tracked pattern2414may be approaching or has hit the ground and, therefore, has changed position in a suspicious manner. According to this embodiment, the processor1410may determine a position of a coordinate2408along a bottom edge of the tracked pattern bounding area2406and a relationship between the position of the coordinate2408along the bottom edge of the tracked pattern bounding area2406and the reference origin for each video frame2401-2403being analyzed. In the example illustrated inFIG. 24, the relationship between the position of the coordinate2408along the bottom edge of the tracked pattern bounding area2406and the reference origin is a distance2412(e.g., pixel distance) between the coordinate2408along the bottom edge of the tracked pattern bounding area2406and a coordinate2410along a bottom edge of the video frame2401-2403(or some other defined area within the frame2401-2403) as defined by the dimensions of the video frame2401-2403. The coordinate2408on the bottom edge of the tracked pattern bounding area2406may be approximately centered along the bottom edge of the tracked pattern bounding area2406. The coordinate2410on the bottom edge of each frame2401-2403may be likewise centered along the bottom edge of the frame2401-2403. Alternatively, the coordinates2408,2410along the bottom edges of the tracked pattern bounding area2406and the frame2401-2403may be off-center. For example, processor1410may select three points along the bottom edge of the tracked pattern bounding area2406(e.g., two corners and the center) and measure distances (e.g., pixel distances) between the selected points and the bottom edge of the frame2401-2403. The processor1410may then select the bounding area bottom edge point that produces the shortest distance as the coordinate on the bottom edge of the tracked pattern bounding area2406for the particular frame2401-2403. In the exemplary scenario depicted inFIG. 24, the coordinate2408on the bottom edge of the tracked pattern bounding area2406may be determined to be centered in frame2401and at a corner in frames2402,2403. By contrast, the coordinate2410on the bottom edge of each frame2401-2403may remain centered in the frame2401-2403. In frame2404, the tracked pattern2414has dropped out of the camera's field of view and, therefore, is not present in the frame2404.

To determine whether the tracked pattern bounding area2406is rapidly approaching the bottom of frames2402,2403over the analyzed, later-in-time video frames2402-2404, the processor1410may use statistical processing to analyze the change in relationship (e.g., distance) between the tracked pattern bounding area2406and the bottom of each frame2402,2403. For example, the processor1410may determine a linear regression from the bounding area edge-to-frame edge distance data to represent how the relationship between the position of the coordinate2408along the bottom edge of the tracked pattern bounding area2406and the position of the coordinate2410along the bottom edge of the frame2402,2403changes across the set of subsequent video frames2402,2403. The processor1410may then determine a gradient for the linear regression and compare the gradient to a threshold. For example, in the context of a person falling to the ground from a standing position, the gradient threshold may be set in the range of −0.50 and −0.75, which equates to a 50% to 75% decrease in distance per second. When the gradient is less than its threshold, the processor1410may determine that the tracked pattern bounding area2406is moving downward rapidly over the predetermined time period. Alternatively, the processor1410may, upon detecting that the gradient is below its threshold, analyze video data for additional video frames (e.g., video frame2404) to further assist in determining whether the tracked pattern2414is no longer detectable or whether the tracked pattern2414or its bounding area2404is at or near the bottom of the video frames and not changing/moving. The combination of rapid downward motion of the tracked pattern2414over the predetermined period of time and subsequent loss of detection or non-movement of the tracked pattern2414may be used as a trigger to communicate an emergency message to an emergency management system for a potential man-down situation.

FIG. 25provides yet another exemplary illustration for how the system processor1410may analyze a set of received video frames to perform suspicious activity detection and pattern tracking in connection with the traffic stop use case ofFIG. 18. According to this embodiment, the processor1410receives streaming video data from a camera (e.g., the rearward-directed camera1809or the multi-directional camera1808) arranged to capture images from behind the police car1803and extracts therefrom data representing a video frame2501(e.g., Video Frame N inFIG. 25). The processor1410compares the video frame data to data representing a set of one or more predefined patterns stored in memory114(which may be local memory or remote memory). In the illustrated case, the set of predefined patterns includes one or more patterns for an automobile or other vehicle. As discussed above with respect toFIG. 23, automobile patterns may include patterns for various portions or components of the automobile, as well as composite patterns that may include one of more individual automobile components (e.g., an automobile composite front pattern that combines patterns for the roof, windshield, hood, front bumper, headlights (and other front lights), license plate, side view mirrors, front tires, and other identifiable components from the perspective of standing in front of an automobile and looking back toward it). In the example illustrated inFIG. 25, the processor1410determines that the outline of the front of a car2514(which could be car1812fromFIG. 18) is substantially similar to a stored predefined pattern, such as a stored automobile composite front pattern. In response to such determination, the processor1410may define a bounding area2506for the detected pattern2514by bounding the pattern2514with a simpler geometric shape (e.g., a rectangle in this particular case). According to one exemplary embodiment, the processor1410may commence pattern tracking upon detecting the predefined pattern2514within the video frame2501and then defining a tracked pattern bounding area2506for the pattern2514. According to an alternative embodiment having substantially greater processing resources, the automobile composite front pattern2514may be tracked directly without using the easier-to-process bounding area2506.

According to the embodiment illustrated inFIG. 25, the processor1410may set the position of the vehicle (e.g., police car1803) as the reference origin for images captured by the rearward-directed camera1809(or the multi-directional camera1808), if the processor1410hasn't already done so when determining whether to commence pattern tracking. Setting the position of the police car1803as the reference origin provides a point of view for the processor1410to assess suspicious activity from the rear of the vehicle that could affect the police officer operating the car1803, who is the person under surveillance again for this example. To evaluate potential suspicious activity, the processor1410may monitor the size of the tracked pattern bounding area2506over a set of video frames2502-2504that are subsequent to or otherwise later in time than the video frame2501that resulted in commencement of pattern tracking in the first place (three video frames2502-2504are shown in the set of subsequent video frames for illustration, but the set may include ten or more video frames as described above). The set of subsequent/later-in-time video frames2502-2504over which a tracked pattern2514or its bounding area2506is analyzed may be sequential in nature (e.g., using the nomenclature fromFIG. 25, Mxmay equal “1,” Mymay equal “2,” Mzmay equal “3,” and so forth) or may be otherwise selected over the tracking time period (e.g., Mxmay equal “5”, Mymay equal “10,” Mzmay equal “15,” and so forth based on how the video frames to be analyzed are selected). The video frames2501-2504may include video data representing the entire field of view of the applicable camera1809(i.e., within the area defined by the camera lens' horizontal and vertical fields of view) or may only include video data representing a target capture area2204within the overall field of view of the camera1809. Use of a target capture area2204may be applicable when the camera1809is part of a video camera system1900,2000, such as those described above with respect toFIGS. 19-22.

When the size of the tracked pattern bounding area2506becomes progressively larger over the set of subsequent video frames2502-2504(e.g., as illustrated inFIG. 25), the processor1410may determine that the tracked pattern2514is approaching the police car1803and, therefore, has changed position in a suspicious manner. To determine whether the tracked pattern bounding area2306is becoming larger over several video frames, the processor1410may use statistical processing to analyze the measured bounding area sizes. For example, the processor1410may determine a linear regression from the bounding area size data to represent how the size of the tracked pattern bounding area2506changes across the set of subsequent video frames2502-2504. The processor1410may then determine a gradient for the linear regression and compare the gradient to a threshold. For example, in the context of a car1812approaching the police car1803from the rear, the gradient threshold may be set in the range of 0.05 and 0.10, which equates to a 5.0% to 10.0% increase in bounding area size per second. When the gradient is greater than its threshold, the processor1410determines that the tracked pattern bounding area2506is becoming larger over the set of subsequent video frames2502-2504.

Additionally or alternatively, the processor1410may be programmed to determine whether the tracked pattern bounding area2506is becoming progressively closer to a bottom of each frame2502-2504in the subsequent set of video frames2502-2504. For example, where the police car1803is set as the reference origin for images captured by the rearward-directed camera1809(i.e., where the camera1809provides a point of view from the rear of the police car1803), movement of the tracked pattern2514toward the bottom of each video frame over multiple video frames indicates that the tracked pattern2514may be drawing nearer to the police car1803and, therefore, has changed position in a suspicious manner. According to this embodiment, the processor1410determines a position of a coordinate2508along a bottom edge of the tracked pattern bounding area2506and a relationship between the position of the coordinate2508along the bottom edge of the tracked pattern bounding area2506and the reference origin for each video frame2501-2504being analyzed. In the example illustrated inFIG. 25, the relationship between the position of the coordinate2508along the bottom edge of the tracked pattern bounding area2506and the reference origin is a distance2512(e.g., pixel distance) between the coordinate2508along the bottom edge of the tracked pattern bounding area2506and a coordinate2510along a bottom edge of the video frame2501-2504(or some other defined area within the frame2501-2504) as defined by the dimensions of the video frame2501-2504. The coordinate2508on the bottom edge of the tracked pattern bounding area2506may be approximately centered along the bottom edge of the tracked pattern bounding area2506and the coordinate2510on the bottom edge of each frame2501-2504may be likewise centered along the bottom edge of the frame2501-2504. Alternatively, the coordinates2508,2510along the bottom edges of the tracked pattern bounding area2506and the frame2501-2504may be off-center. In the exemplary scenario depicted inFIG. 25, the coordinate2508on the bottom edge of the tracked pattern bounding area2506and the coordinate2510on the bottom edge of each frame2501-2504remain centered in the frame2501-2504. In frame2504, the bottom edge of the tracked pattern bounding area2506is shown to have reached the bottom edge of the frame2504; thus, the coordinate2508on the bottom edge of the tracked pattern bounding area2506and the coordinate2510on the bottom edge of the frame2504are collocated.

To determine whether the tracked pattern bounding area2506is becoming progressively closer to the bottom of frames over the analyzed, later-in-time video frames2502-2504, the processor1410may use statistical processing to analyze the change in relationship (e.g., distance) between the tracked pattern bounding area2306and the bottom of each frame2502-2504. For example, the processor1410may determine a linear regression from the bounding area edge-to-frame edge distance data to represent how the relationship between the position of the coordinate2508along the bottom edge of the tracked pattern bounding area2506and the position of the coordinate2510along the bottom edge of the frame2502-2504changes across the set of subsequent video frames2502-2504. The processor1410may then determine a gradient for the linear regression and compare the gradient to a threshold. For example, in the context of a car1812approaching the stopped police car1803, the gradient threshold may be set in the range of −0.10 and −0.20, which equates to a 10% to 20% decrease in distance per second. When the gradient is less than its threshold, the processor1410may determine that the tracked pattern bounding area2506is becoming closer to the bottom of each frame2502-2504(and, therefore, closer to the reference origin, such as the rear of the police car1803) over the set of subsequent video frames2502-2504. The processor1410may analyze bounding area size changes, bounding area positioning relative to a reference origin or other reference point, both bounding area size changes and bounding area positioning, and/or any other video data-based characteristics to make its final determination as to whether a tracked pattern has changed position in a suspicious manner.

In addition to detecting and analyzing an automobile composite front pattern2514for purposes of determining whether an approaching car1812may pose a threat to a police officer executing a traffic stop, the processor1410may detect and analyze individual component patterns within the composite pattern2514. For example, the processor1410may compare video frame data to data representing a license plate pattern stored in memory114. For example, the processor1410may compare the various components of the automobile composite front pattern2514to isolate a license plate2520. Where such a license plate pattern is detected, the processor1410may communicate an image of the license plate2520to a motor vehicle department computer system for further analysis.

The suspicious activity detection and pattern tracking process described above with respect toFIG. 25may also or alternatively be performed by the processor1410or another processor (such as a processor of the camera capturing the video), where the video data analyzed in the process is captured by a camera secured to the body of the person under surveillance. In other words, the process ofFIG. 25may be similarly applied to video data supplied by the officer's, or another wearer's, body camera (e.g., camera501) from the scene of an incident, such as a traffic stop. The application of such a process to body cam-supplied video data was described above in an exemplary manner with respect toFIG. 6. In this case, the predefined patterns may include component patterns (e.g., vehicle components, human body components, etc.) and composite patterns (e.g., vehicle composite patterns, human body composite patterns, etc.) as generally described above.

FIG. 26illustrates a process flow diagram2600of steps executed by a processor1410of a video processing system1400, which is performing the target capture area maintenance/correction process ofFIG. 21, to determine whether a tracked pattern in one or more received video streams has changed positioned in a suspicious manner, in accordance with yet another exemplary embodiment of the present disclosure. According to this embodiment, the processor1410receives (2601) one or more video data streams from one or more motor vehicle video cameras101-104, such as the police car cameras1807-1809shown inFIG. 18. For example, the processor1410may receive video data from a rear-view mirror video camera system1900,2000serving as the forward-directed camera1807of the police car1803, which in turn is a camera101of the video processing system1400.

In addition to receiving a video data stream from the motor vehicle camera1807, the processor1410receives (2603) sensor data from a motion-sensing subsystem1401of the video processing system1400. The processor1410uses the sensor data in the process discussed above with respect toFIG. 21to determine a target capture area2214within the video data. Where the video processing system1400further includes audio detection capability (e.g., one or more microphones1402), the processor1410may receive an audio data stream that is time-synchronized with the video data stream. The audio data may be analyzed and used to insert markers into the video data as discussed above with regard toFIG. 15.

Having identified the target capture area2214, the processor1410selects (2605) data from the target capture area2214representing a set of one or more video frames based on the video streaming protocol and the video codec used by the camera1807and the video processor1410. Responsive to selecting target capture area video data for a first set of video frames, the processor1410determines (2607) whether the video frame data includes data representing one or more predefined patterns. As discussed above with respect toFIGS. 1, 4, 9, and 15, the processor1410may compare portions of the video frame data to data representative of a set of predefined patterns previously stored in memory114to determine whether a video frame or any portion thereof includes data substantially similar to data representing a predefined pattern. The predefined patterns may include, inter alia, object patterns, animal patterns, general human image patterns, and specific human image patterns. For example, the system memory114may include one or more databases of human image patterns representing images of persons who may be subject to surveillance by the video processing system1400over time.

When the video frame data does not include data representing one or more predefined patterns, the processor1410selects (2609) data from the target capture area2214representing a next set of one or more video frames and determines (2607) whether that video frame data includes data representing one or more predefined patterns. When the target capture area video data for the first set of video frames includes data representing one or more predefined patterns (or when the target capture area video data for a later set of video frames includes predefined pattern data where the target capture area video data for an earlier set of video frames did not), the processor1410commences tracking (2611) of the detected pattern or patterns within the target capture area video data and selects (2613) data from the target capture area2214representing one or more subsequent or otherwise later-in-time sets of video frames from the video data stream.

The processor1410analyzes the later-in-time video frame data to determine (2615) whether such video frame data continues to include the tracked pattern or patterns. Pattern tracking may be performed using bounding areas, such as those described above with respect toFIGS. 3, 6, 7, 16, 17, and 23-25. For example, a bounding area may be defined by the processor1410for each predefined pattern that is detected. The bounding areas may then be monitored for changes over time to determine whether a tracked pattern changes position in a suspicious manner. The process of defining bounding areas and using them for identification and tracking purposes substantially reduces the processing resources necessary to reliably track patterns over large quantities of video frames.

If target capture area video data for the subsequent set of video frames includes the tracked pattern or patterns, the processor1410determines (2617) whether the tracked pattern(s) has changed position in a suspicious manner. Otherwise, the processor1410selects (2605) video data from the target capture area2214representing the next subsequent set of one or more video frames and the process repeats from decision block2607.

To determine whether a tracked pattern has changed position in a suspicious manner, the processor1410analyzes movement of the tracked pattern over multiple video frames. For example, the processor1410may determine, based on the tracking, whether the tracked pattern is moving toward the person under surveillance, moving away from the person under surveillance, falling down, getting up, moving left, moving right, and so forth. According to one exemplary embodiment, the video processor1410may utilize a process similar to the one described above with respect toFIG. 6to analyze video data from a camera (e.g., camera101) positioned in or on the motor vehicle (e.g., vehicle1803) that transported the person under surveillance to the current location. The processor1410may determine from the video data analysis that the tracked pattern is approaching or moving away from the person under surveillance and/or the stopped motor vehicle, either of which may be deemed a suspicious change of position of the tracked pattern depending on other factors, such as the position and rate of approach or departure, and/or the presence of another predefined pattern in the video data (e.g., the pattern for a weapon). The processor1410may alternatively or additionally determine from the video data analysis that a tracked pattern, such as a door or window, has opened or closed, which may be considered suspicious depending on the context as determined by the processor1410based on other image patterns detected in the video data and/or audio patterns detected in received audio data.

A variety of exemplary processes for determining whether a tracked image pattern has changed position in a suspicious manner are described above. Such processes may be applied in connection with decision block2617ofFIG. 26where the video data used in such processes is from a target capture area2214that is less than the area defined by the horizontal and vertical fields of view2202,2203of the camera's lens1915,2015.

When the processor1410determines that one or more tracked patterns have changed position in a suspicious manner, the processor1410communicates (2619) an alert to the person under surveillance and/or a third party (e.g., an emergency management system) as to the suspicious activity. For example, the processor1410may activate a local alert, such as activate an audible and/or visual alarm or send an audio message to a local sound speaker, to notify the person under surveillance (e.g., the police officer or officers on scene). Alternatively, the processor1410may communicate, via the communication interface108, an alert message to a mobile application executing on a wireless communication device carried by the person under surveillance (e.g., smartphone, cellular phone, tablet computer, personal digital assistant). In the latter case, the alert message may cause the mobile application to activate an audible alarm and/or a haptic alarm of the wireless communication device to notify the person of the potential threat. Still further, the processor1410may communicate, via the communication interface108, at least some of the video data from the analyzed video stream (e.g., the last ten seconds or 300 video frames) to a mobile video processing and display application executing on a wireless communication device carried by the person under surveillance. In this case, the mobile application may be configured to automatically play and display the received video to enable the person under surveillance to assess the potential threat and react thereto as necessary. Still further, the processor1410may communicate, via the communication interface108, an emergency message to a remote emergency management system to inform an operator of the system (e.g., a police office or 911 emergency operator) as to potential suspicious activity at the location of the person under surveillance, including, without limitation, the possibility of a man-down, injured officer, or other urgent situation. The emergency alert message may include the video data that served as the basis for the processor1410to issue the emergency alert message.

In addition to detecting and analyzing target capture area video data to determine whether such data shows a predefined pattern moving suspiciously, the processor1410may analyze target capture area video data to detect individual component patterns within a composite predefined pattern, such as a composite front pattern2514or a composite rear pattern2314for a vehicle. For example, the processor1410may compare target capture area video data to data representing a license plate pattern stored in memory114. Where such a license plate pattern is detected, the processor1410may communicate an image of the license plate2320,2520to a motor vehicle department computer system for further analysis.

Additional embodiments of the processes and systems disclosed above may perform various additional functions and provide a variety of additional features in connection with using video analysis and pattern tracking to monitor for suspicious activity and otherwise serve to protect a person under surveillance. For example, according to one additional embodiment, the video processing system1400(e.g., through operation of the processor1410) may determine whether the motor vehicle (e.g., police car1803) that includes the video camera101or cameras101-104(e.g., cameras1807-1809) has come to a stop and, if so, activate the video camera(s)101-104. In other words, according to this embodiment, the vehicle-based cameras would be automatically activated when the vehicle stopped. To determine that the vehicle has stopped, video processing system1400may utilize the motion-sensing subsystem1401and the processor1410. For example, the processor1410may determine that the vehicle stopped based on sensor data received from the motion-sensing subsystem1401. Alternatively, the processor1410may be connected to the vehicle's on-board diagnostic system to enable the processor1410to detect when the vehicle has stopped.

According to another embodiment, the cameras101-104of the video processing system100,1400may include a body camera501,1301secured to the body of the person under surveillance, and the video processing system100,1400, through operation of its processor110,1410, may remotely activate the body camera responsive to determining that received video data representing a set of one or more video frames includes data representing one or more predefined patterns. In other words, according to this embodiment, the video processor110,1410remotely actives the body camera501,1301after detecting the presence of one or more predefined patterns in video data received from one or more other cameras101-104,502-510,1807-1809. To remotely activate the body camera, the video processor110,1410may communicate an activation signal to the body camera501,1301via the communication interface108. After the body camera is activated, it becomes an active camera in the video processing system100,1400and communicates video data to the video processor100,1400. The video processor100,1400may then record the body cam video data in memory114.

According to yet another embodiment, the video processing system100,1400may be used to detect and report a rollover or other sudden impact to a vehicle monitored by the video processing system100,1400. For this embodiment, the video processing system100,1400includes or is coupled to one or more motion-sensing subsystems1401. The motion-sensing subsystem1401may be incorporated into a camera101,502,1807or may be installed elsewhere in the vehicle. According to this embodiment, the video processing system100,1400, through operation of its processor110,1410, receives sensor data from at least one motion-sensing subsystem1401. The sensor data may indicate changes in inertia and other movement of the motion-sensing subsystem1401. Responsive to receiving sensor data indicating a rapid change in inertia of the video camera101,502,1807, the motor vehicle1803,522in which a person under surveillance (e.g., police officer, guard, messenger, courier, etc.) is travelling, or both, the video processing system may determine an orientation of the motor vehicle based upon such sensor data. In other words, depending on the configuration of the motion-sensing subsystem1401, the sensor data supplied by the motion-sensing subsystem1401may enable to determine whether the vehicle rolled over and now remains upright, on its side, or upside down. The processor110,1410may then communicate an emergency message to an emergency management system responsive to determining that the orientation of the motor vehicle is abnormal (e.g., on its side or upside down) or that the change in inertia indicates a rollover has occurred. Therefore, the video processing system100,1400may include or interact with a motion-sensing subsystem1401to monitor for accidents or other incidents involving a vehicle that includes one or more cameras502,1807-1809forming part of the video processing system100,1400. Upon detecting such an incident, an emergency message may be sent to emergency management authorities to facilitate expedited action to be taken.

According to yet another embodiment, the video processing system100,1400may, through operation of its processor110,1410, insert and store a digital marker in video data received from a camera101-104,502,1807-1809responsive to receiving sensor data indicating a rapid change in inertia of the video camera101,502,1807-1809, the motor vehicle1803,522in which a person under surveillance (e.g., police officer, guard, messenger, courier, etc.) is travelling, or both. In other words, the video processor110,1410may insert and store a digital marker in video data received by a camera101,502,1807-1809so as to identify the time at which the processor110,1410received sensor data from a motion-sensing subsystem1401, which sensor data indicated a rapid change in inertia of the video camera101,502,1807-1809, the motor vehicle1803,522, or both. Marking the video in such a manner enables a person later investigating the accident or other incident to quickly view stored video from the time at which the incident occurred.

According to yet another embodiment, the video processing system100,1400may, through operation of its processor110,1410, provide man-down detection and reporting after a rollover or other incident involving a vehicle transporting a person under surveillance by the video processing system100,1400. According to this embodiment, at least one of the system cameras101-104has a video capture area that includes an area within a cabin of the motor vehicle1803,522. Responsive to receiving sensor data from the motion-sensing subsystem1401indicating a rapid change in inertia of the video camera101,502,1807-1809, the motor vehicle1803,522, or both, the video processor110,1410may determine from video data capturing the inside of the vehicle's cabin whether a portion of a body of the person under surveillance is present within the video capture area(s) of the camera(s) and is moving. If, through analyzing the video data for the vehicle cabin, the video processor110,1410determines that a portion of the body of the person under surveillance is within the vehicle's cabin but not moving, the video processor110,1410may communicate, via the communication interface108, an emergency message to an emergency management system. Thus, according to this embodiment, the video processing system100,1400can be used to monitor and report emergency situations related to vehicular accidents involving a person under surveillance when the person appears to be seriously injured during the accident.

According to yet another embodiment, the video processing system100,1400may, through operation of its processor110,1410and the communication interface108, be informed as to the status of system cameras101-104,502,1807-1809through receipt of messages indicating whether the cameras (e.g., image sensors) are active or inactive (i.e., on or off). The processor110,1410can delay receiving video data for a camera until it first receives a data message from the camera indicating that the camera is active. Thus, the video processor110,1410can withhold allocating resources to process video data from a camera until the camera has notified the video processor110,1410that the camera is active. Additionally, if the video processor110,1410determines that it has not received, within a preset amount of time (e.g., a preset amount of time after the video processor110,1410detects that it is within communication range of the camera), a status message from the camera indicating that the camera is active, the video processor110,1410may communicate a control message to the camera instructing the camera to activate and begin communicating video data to the video processor110,1410. For example, where the system cameras include a body camera501secured to the body of a person, which may be the person under surveillance, and a data message from the body camera501does not indicate that the body camera has been activated, the video processor110,1410may communicate a control message to the body camera501causing the body camera501to activate and begin communicating video data to the video processor110,1410. Such a procedure may be used to keep the body camera501from transmitting video until instructed to do so in order to conserve the body cam's battery or to delay body cam transmissions until one or more other cameras are also transmitting, such as the vehicle-mounted cameras1807-1809.

While several examples have been provided above with respect to detecting and tracking objects and people in connection with detecting suspicious activity and potential threats, the attached independent claims are not intended to be limited to such examples unless such claims include expressly limiting language. The disclosed examples are merely intended to assist those of skill in the art with an understanding of the various processes and systems that may be constructed using video analysis to track and detect suspicious activity and/or potential threats while conducting safety monitoring of a person under surveillance.

The present disclosure describes automated, human intervention-less, video analysis-based suspicious activity detection systems and methods. With such systems and methods, video data may be analyzed locally or in the cloud to determine, in real time or near real time, the presence of a potential threat or other suspicious behavior to a person located in or proximate to the video capture area(s) of camera(s) that produced the analyzed video data. Where suspicious behavior is detected, the systems and methods may alert the person under surveillance or an emergency management system in real time or near real time to give the person an opportunity to take defensive action or to allow emergency personnel to quickly respond to the suspicious activity. The systems and methods may also forward the received videos, as optionally augmented to include overlays highlighting the pattern or patterns being tracked as suspicious, to security or emergency personnel so as to enable such personnel to promptly respond to the activity. The systems and methods described herein are particularly, though not exclusively, advantageous for enhancing the protection of persons involved in providing cash management or transport services, package delivery services, public safety services, and other services that are provided in a mobile manner and have a higher than normal risk of being subject to criminal or other illicit activity.

As detailed above, embodiments of the disclosed systems and methods reside primarily in combinations of method steps and apparatus components related to detecting potential threats to persons based on real-time or near real-time video analysis. Accordingly, the apparatus components and method steps have been represented, where appropriate, by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

In this document, the drawings, and the appended claims, relational terms such as “first” and “second,” “top” and “bottom,” and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains,” “containing,” and any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, apparatus, or system that comprises, includes, has, or contains a list of elements, characteristics, or features does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, apparatus, or system. The term “plurality of” as used in connection with any object or action means two or more of such object or action. A claim element proceeded by the article “a” or “an” does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, apparatus, or system that includes the element.

In the foregoing specification, specific embodiments of the claimed invention have been described. However, one of ordinary skill in the art will appreciate that various modifications and changes can be made without departing from the scope of the present invention as set forth in the appended claims. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of claimed invention. For example, it is expected that one of ordinary skill in the art, notwithstanding possibly significant effort and many design choices motivated by, for example, available time, current technology, and economic considerations, when guided by the concepts and principles disclosed herein will be readily capable of generating software instructions or programs and configuring integrated circuits and other hardware to implement the methods and systems recited in the appended claims without undue experimentation. The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as critical, required, or essential features or elements of any or all the claims. The present invention is defined solely by the appended claims including any amendments made during the pendency of this application and all equivalents of those claims as issued.