Patent Publication Number: US-10789840-B2

Title: Systems, apparatuses and methods for detecting driving behavior and triggering actions based on detected driving behavior

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a Continuation-in-Part of, and claims priority to, U.S. patent application Ser. No. 15/413,205, entitled “Systems, Apparatuses and Methods for Triggering Actions Based on Data Capture and Characterization,” filed on Jan. 23, 2017, which in turn claims the benefit of U.S. Provisional Patent Application No. 62/333,818, entitled “Systems, Apparatuses and Methods for Creating, Identifying, Enhancing, and Distributing Evidentiary Data,” filed on May 9, 2016. U.S. patent application Ser. No. 15/413,205 and U.S. Provisional Patent Application No. 62/333,818 are hereby incorporated herein by reference in their entirety. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH 
     Not Applicable. 
     TECHNICAL FIELD OF THE INVENTION 
     This disclosure relates generally to systems and methods for collecting data related to a driver, analyzing the collected data to determine the driving behavior of a driver and triggering actions in response to driving behavior. More particularly, but not by way of limitation, this disclosure relates to using sensors and cameras to capture real-world image data, making determinations in real-time pertaining to the captured data, and triggering actions based upon such determinations. 
     BACKGROUND 
     Law enforcement officers (“LEOs”) have a higher risk of injury or death than most other occupations. Among the more dangerous activities LEOs engage in involves driving. For example, According to the United States Bureau of Labor Statistics, 14% of all nonfatal injuries, and 41% of all fatal injuries to LEOs were caused by transportation incidents in 2014. See Bureau of Labor Statistics, https://www.bls.gov/iif/oshwc/cfoi/police-officers-2014-chart5-data.htm. Vehicle accidents involving LEOs can occur for the same reasons as other drivers such as distracted driving, speeding, reckless or aggressive driving, weather, running red lights and stop signs, fatigue, or carelessness. 
     Additionally, several causes of vehicle accidents are unique to the functions of LEOs, such as high-speed pursuits, or responding to emergencies. LEOs have various means of technology at their disposal to perform their tasks. However, while technology has provided law enforcement officers powerful tools to perform their jobs, it has also added a level of complexity for officers on patrol. There are many distractions competing for an officer&#39;s attention. “Workload” refers to the tasks which an LEO must perform in a short amount of time. For example, an LEO may be driving, observing driving behavior of others, listening to a dispatch radio, talking on the radio to other LEOs, reviewing a Be on Look Out (“BOLO”) alert, and manipulating certain auxiliary vehicle controls such as a light bar or spotlight. LEOs frequently also have vehicle-mounted computers which they may view or be required to type upon. 
     High-speed pursuits introduce the highest risk for death or injury to LEOs, the suspect(s) and the public. High-speed pursuits present a unique and complicated set of risks which must be balanced against the objectives of the pursuit (e.g. suspect apprehension). Specifically, high-speed pursuits place the LEO, suspects, victims, bystanders, and the community at greater risk of harm of vehicle accidents. LEOs must process factors including, but not limited to, the LEO&#39;s own driving skills, traffic, weather, terrain, road conditions, proximity to other vehicles and bystanders, proximity of other LEOs available for backup, avenues for escape by the suspect, and the potential for apprehension (i.e. the officer must consider the likelihood of apprehension in the decision to continue a chase). The LEO must weight these factors to determine whether a suspect can be pursued without excessive risk. Many law enforcement agencies have guidelines, training and officer education intended to address these risks. 
     Many high-speed pursuits are initiated in response to routine traffic stops. For example, an LEO may observe a minor traffic offense such as running a red light. When the LEO turns on the police car&#39;s emergency lights or siren to signal to the suspect to pull over, the suspect may flee. Alternatively, the suspect may initially pull over and wait for the officer to exit his or her vehicle before fleeing. In either case, the LEO is frequently unaware of the suspect&#39;s identity or the level of risk the suspect poses to the public. For example, a suspect wanted for a violent crime may present a greater risk to the public if he is not apprehended. In such a case, the interests of law enforcement (i.e. suspect apprehension) might militate in favor of pursing the suspect. Where the suspect is not suspected of prior criminal conduct, and the suspect cannot be pursued safely, some law enforcement policies may advise the pursing LEO to “give up” with the hope that the suspect will cease driving recklessly once he or she recognizes the pursuit has terminated. Many law enforcement agencies have guidelines, training and officer education intended to address these risks and provide guidance when, or if, an LEO should cease a pursuit. 
     High-speed pursuits are times of intense workload for an LEO. Furthermore, during a pursuit, a person&#39;s stress response (i.e. fight-or-flight response) causes the adrenal gland to release adrenaline, dilate the pupils and constrict blood vessels. This physiological change causes heightened focus on the fleeing suspect, but reduces the LEO&#39;s peripheral awareness of the environment around the LEO. This physiological condition is referred to as “tunnel vision.” The dramatic increase in the LEO&#39;s workload simultaneously occurring with the LEO&#39;s tunnel vision can cause the LEO to overlook certain risk factors associated with a pursuit and result in excessive risk to the LEO or the public. 
     Therefore, systems, apparatuses, and methods are discussed to monitor an LEO&#39;s driving behavior, monitor the driving behavior of other drivers (including a suspect), and trigger various events based upon the behavior of each. Also discussed are systems, apparatuses, and methods of a tiered analysis of the LEO&#39;s driving behavior that considers the dichotomy between the interests of law enforcement and the interests of public safety and the safety of the LEO. 
     SUMMARY 
     In view of the aforementioned problems and trends, embodiments of the present invention provide systems and methods for detecting, monitoring, evaluating and triggering actions in response to the driving behavior of a driver and/or a third party. 
     According to an aspect of the invention, a system includes a vehicle, the vehicle including at least one camera device configured to capture image data, at least one sensor configured to capture vehicle telemetry data, and a microprocessor linked to the at least one camera device and the at least one sensor, wherein the microprocessor is configured with instructions to detect the occurrence of at least one designated event. 
     According to another aspect of the invention, a method includes capturing image data using a camera device mounted to a vehicle, sampling telemetry data from at least one sensor mounted to the vehicle, and detecting the occurrence of at least one designated event from the image data and/or the telemetry data. 
     According to another aspect of the invention, a method includes capturing image data using a camera device, wherein the camera device is mounted to a first vehicle; sampling telemetry data from at least one sensor, wherein the at least one sensor is mounted to the first vehicle; determining an applicable speed limit; determining a first speed of the first vehicle using at least one of (i) the image data captured by the camera mounted to the first vehicle, and (ii) the telemetry data sampled from the at least one sensor mounted on the first vehicle; calculating a relative speed of a second vehicle to the first vehicle using the image data captured by the camera device mounted to the first vehicle; calculating a second speed of the second vehicle by adding together (i) the relative speed of the second vehicle to the first vehicle and (ii) the first speed of the first vehicle; and comparing the second speed of the second vehicle with the applicable speed limit. 
     According to another aspect of the invention, a system includes a first vehicle, the first vehicle including at least one camera device configured to capture image data, at least one sensor configured to capture vehicle telemetry data, and a microprocessor linked to the at least one camera device and the at least one sensor, wherein the microprocessor is configured with instructions to: determine an applicable speed limit; determine a first speed of the first vehicle using at least one of (i) image data captured by the camera device, and (ii) vehicle telemetry data captured by the at least one sensor; calculate a relative speed of a second vehicle to the first vehicle using the image data captured by the camera device; calculate a second speed of the second vehicle by adding together (i) the relative speed of the second vehicle to the first vehicle and (ii) the first speed of the first vehicle; and compare the second speed of the second vehicle with the applicable speed limit. 
     Other aspects of the embodiments described herein will become apparent from the following description and the accompanying drawings, illustrating the principles of the embodiments by way of example only. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The following figures form part of the present specification and are included to further demonstrate certain aspects of the present claimed subject matter, and should not be used to limit or define the present claimed subject matter. The present claimed subject matter may be better understood by reference to one or more of these drawings in combination with the description of embodiments presented herein. Consequently, a more complete understanding of the present embodiments and further features and advantages thereof may be acquired by referring to the following description taken in conjunction with the accompanying drawings, in which like reference numerals may identify like elements, wherein: 
         FIG. 1 , in accordance with some embodiments of the present disclosure, depicts a communication scheme and data flow/exchange, specifically between a police vehicle with an onboard camera device and a police station; 
         FIG. 2 , in accordance with some embodiments of the present disclosure, depicts the recognition of a plurality of “vehicle” shapes in a video frame of visual data; 
         FIG. 3 , in accordance with some embodiments of the present disclosure, depicts the recognition of various traffic signal shapes in a video frame of visual data; 
         FIG. 4 , in accordance with some embodiments of the present disclosure, depicts the recognition of a plurality of “people” shapes in a video frame of visual data; 
         FIG. 5 , in accordance with some embodiments of the present disclosure, depicts a processing flow chart for an analytics engine; 
         FIG. 6 , in accordance with some embodiments of the present disclosure, depicts a top view of a device that may be installed on top of a police vehicle, featuring at least one 360-degree spherical camera; 
         FIG. 7 , in accordance with some embodiments of the present disclosure, depicts a bottom view of the device of  FIG. 6 , receiving sound data, and various pictorial depictions of sound data that may be collected; 
         FIG. 8 , in accordance with some embodiments of the present disclosure, depicts the transfer of video and pictorial data from the spherical camera of the device of  FIG. 6 , including the receiving of video/pictorial data, and various exemplary depictions of visual data that may be collected and subsequently isolated; 
         FIG. 9 , in accordance with some embodiments of the present disclosure, is a flow schematic of sound data that may be collected from one or more microphones, that is subsequently processed (audio signal processing) to isolate parameters of interest (for example, direction of a loud noise or gunshot detection), and subsequent related action(s) e.g. video recording trigger/emergency announcement in response to gunshot detection or changes to a 360-degree spherical camera via video angle software in order to determine the direction of a loud noise; 
         FIG. 10 , in accordance with some embodiments of the present disclosure, depicts data flow, specifically video data flow/exchange between a police vehicle with In Car Video (ICV) and Access Point (AP), that is further remotely controlling and exchanging information with various Body Worn Cameras (BWC) and other ICV units in other vehicles; 
         FIG. 11 , in accordance with some embodiments of the present disclosure, depicts a Global Data Access System configuration; 
         FIG. 12 , in accordance with some embodiments of the present disclosure, is a flow chart depicting, at a top level, a method for triggering an action in response to capture and characterization of image data; 
         FIG. 13 , in accordance with some embodiments of the present disclosure, is a flow chart depicting, at a top level, another method for triggering an action in response to capture and characterization of image data; 
         FIG. 14 , in accordance with some embodiments of the present disclosure, depicts a system including a vehicle with a camera, sensors and a computer, for evaluating driving behavior and triggering actions based upon the evaluated behavior; 
         FIG. 15 , in accordance with some embodiments of the present disclosure, depicts a scene of a video frame captured by the onboard camera of a police vehicle illustrating the instructions executed by an onboard computer to recognize a speed limit sign and evaluate a driver&#39;s driving behavior in relation to the speed limit sign; 
         FIG. 16 , in accordance with some embodiments of the present disclosure, depicts a scene of a video frame captured by the onboard camera of a police vehicle illustrating the instructions executed by an onboard computer to recognize a stop sign and evaluate a driver&#39;s driving in relation to the stop sign; 
         FIG. 17 , in accordance with some embodiments of the present disclosure, depicts a scene of a video frame captured by the onboard camera of a police vehicle illustrating the instructions executed by an onboard computer to recognizing a traffic light and evaluate a driver&#39;s driving in relation to the traffic light; 
         FIG. 18 , in accordance with some embodiments of the present disclosure, depicts an accelerometer mounted onto a vehicle used by an onboard computer to evaluate a driver&#39;s driving behavior based on the acceleration of the vehicle in a number of directions; 
         FIG. 19 , in accordance with some embodiments of the present disclosure, is a flow chart of a method of detecting events based on the capture of image and telemetry data; and 
         FIG. 20 , in accordance with some embodiments of the present disclosure, is a flow chart of a method of determining the speed of a second vehicle based on image data and telemetry data captured by a first vehicle. 
     
    
    
     NOTATION AND NOMENCLATURE 
     Certain terms are used throughout the following description and claims to refer to particular system components and configurations. As one skilled in the art will appreciate, the same component may be referred to by different names. This document does not intend to distinguish between components that differ in name but not function. In the following discussion and in the claims, the terms “including” (and the like) and “comprising” (and the like) are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . .” Also, the term “couple,” “coupled,” or “linked” is intended to mean either an indirect or direct electrical, mechanical, or wireless connection. Thus, if a first device couples to or is linked to a second device, that connection may be through a direct electrical, mechanical, or wireless connection, or through an indirect electrical, mechanical, or wireless connection via other devices and connections. 
     As used throughout this disclosure the term “computer” encompasses special purpose microprocessor-based devices such as a digital video surveillance system primarily configured for executing a limited number of applications, and general purpose computers such as laptops, workstations, or servers which may be configured by a user to run any number of off the shelf or specially designed software applications. Computer systems and computer devices will generally interact in the same way with elements and aspects of disclosed embodiments. This disclosure also refers to memory or storage devices and storage drives interchangeably. In general, memory or a storage device/drive represents a medium accessible by a computer (via wired or wireless connection) to store data and computer program instructions. It will also be appreciated that use of the term “microprocessor” in this disclosure encompasses one or more processors. 
     The terms “video data” and “visual data” refer to still image data, moving image data, or both still and moving image data, as traditionally understood. Further, the terms “video data” and “visual data” refer to such image data alone, i.e., without audio data and without metadata. The term “image data” (in contrast to “still image data” and “moving image data”) encompasses not only video or visual data but also audio data and/or metadata. That is, image data may include visual or video data, audio data, metadata, or any combination of these three. This image data may be compressed using industry standard compression technology (e.g., Motion Picture Expert Group (MPEG) standards, Audio Video Interleave (AVI), etc.) or another proprietary compression or storage format. The terms “camera,” “camera device,” and the like are understood to encompass devices configured to record or capture visual/video data or image data. Such devices may also be referred to as video recording devices, image capture devices, or the like. Metadata may be included in the files containing the video (or audio and video) data or in separate, associated data files, that may be configured in a structured text format such as eXtensible Markup Language (XML). 
     The term “metadata” refers to information associated with the recording of video (or audio and video) data, or information included in the recording of image data, and metadata may contain information describing attributes associated with one or more acts of actual recording of video data, audio and video data, or image data. That is, the metadata may describe who (e.g., Officer ID) or what (e.g., automatic trigger) initiated or performed the recording. The metadata may also describe where the recording was made. Metadata may also include telemetry or other types of data. For example, location may be obtained using global positioning system (GPS) information or other telemetry information. The metadata may also describe why the recording was made (e.g., event tag describing the nature of the subject matter recorded). The metadata may also describe when the recording was made, using timestamp information obtained in association with GPS information or from an internal clock, for example. Metadata may also include information relating to the device(s) used to capture or process information (e.g. a unit serial number). From these types of metadata, circumstances that prompted the recording may be inferred and may provide additional information about the recorded information. This metadata may include useful information to correlate recordings from multiple distinct recording systems as disclosed herein. This type of correlation information may assist in many different functions (e.g., query, data retention, chain of custody, and so on). The metadata may also include additional information as described herein, such as: location and size of an object of interest on screen, object&#39;s color and confidence level, vehicle make and confidence level, vehicle type and confidence level, license plate number/state (e.g., which of the 50 US states) and confidence level, and number of pedestrians. The terms “license plate number,” “license plate character,” and the like are all understood to encompass both numbers and other characters on a license plate. 
     The terms “cloud” and “cloud storage” are used interchangeably in this disclosure to describe that data is stored in an area generally accessible across a communication network (which may or may not be the Internet). A “cloud” may refer to a public cloud, private cloud, or combination of a public and private cloud (e.g., hybrid cloud). The term “public cloud” generally refers to a cloud storage area that is maintained by an unrelated third party but still has certain security measures in place to ensure that access is only allowed to authorized users. The term “private cloud” generally refers to a cloud storage area that is maintained by a related entity or that is maintained on physical computer resources that are separate from any unrelated users. 
     The term “global” refers to worldwide and the term “global access” refers to being available or accessible from anywhere in the world via conventional communication means (e.g. the communication network described herein). 
     The term “telemetry data” refers to the data sampled from sensors which measure the parameters of a vehicle or its system and components. 
     The term “vehicle dynamics” refers to how the performance of the vehicle is controlled to accelerate, brake, or steer the vehicle. 
     The term “aggressive driving” refers to the behavior of a driver which tends to endanger the driver, other persons, or property. 
     DETAILED DESCRIPTION 
     The foregoing description of the figures is provided for the convenience of the reader. It should be understood, however, that the embodiments are not limited to the precise arrangements and configurations shown in the figures. Also, the figures are not necessarily drawn to scale, and certain features may be shown exaggerated in scale or in generalized or schematic form, in the interest of clarity and conciseness. The same or similar parts may be marked with the same or similar reference numerals. 
     While various embodiments are described herein, it should be appreciated that the present invention encompasses many inventive concepts that may be embodied in a wide variety of contexts. The following detailed description of exemplary embodiments, read in conjunction with the accompanying drawings, is merely illustrative and is not to be taken as limiting the scope of the invention, as it would be impossible or impractical to include all of the possible embodiments and contexts of the invention in this disclosure. Upon reading this disclosure, many alternative embodiments of the present invention will be apparent to persons of ordinary skill in the art. The scope of the invention is defined by the appended claims and equivalents thereof. 
     Illustrative embodiments of the invention are described below. In the interest of clarity, not all features of an actual implementation are necessarily described for each embodiment disclosed in this specification. In the development of any such actual embodiment, numerous implementation-specific decisions may need to be made to achieve the design-specific goals, which may vary from one implementation to another. It will be appreciated that such a development effort, while possibly complex and time-consuming, would nevertheless be a routine undertaking for persons of ordinary skill in the art having the benefit of this disclosure. It will also be appreciated that the parts and component dimensions of the embodiments disclosed herein may not be drawn to scale. 
     Although the content of this disclosure is presented largely in the context of law enforcement, it should be understood that this content is also applicable outside of that context. For example, this content is also applicable in the consumer and commercial context, e.g., detecting driver behavior with regard to consumer and commercial vehicles. 
       FIG. 1  depicts an embodiment of this disclosure. A police vehicle  10  is equipped with a computer  12  (which accordingly may be referred to as “vehicle  10  computer  12 ”) linked to a server  15  housed in a police station  14  via a communication network  18  (e.g., Internet, Cloud, radio network, Bluetooth, Wi-Fi, 3G, 4G, LTE, satellite, etc.). The computer  12  is linked with a communications module that includes a (e.g., mobile) receiver  13 . The police vehicle  10  may be a car, motorcycle, bicycle, aircraft, watercraft, or other transportation means. The police station  14  may also house a memory storage bank  17  in addition to server  15  for data processing and communications. The vehicle  10  is equipped with one or more camera devices  16  to capture image data from the real world. The camera device(s)  16  may or may not be mobile. The camera device  16  may also be configured with internal memory (e.g. hard disk) and/or coupled to a local or remote storage drive for recordation (in digital or analog format) of the collected information. Suitable camera devices  16  that may be used to implement embodiments of this disclosure include the devices commercially available from COBAN Technologies Inc., in Houston, Tex. (http//www.cobantech.com). 
     The vehicle  10  computer  12  is configured to access one or more databases (onboard the vehicle  10  or remote via the communication network  18 ) containing a repository with detailed information and data of existing vehicles, structures, objects, people, etc.). For example, an accessible database may be populated with data regarding parameters, shapes, other information relating to particular individuals, states and cities, vehicle identification parameters/characteristics (makes, models, colors, etc.), weapons data, etc. The database(s) can be updated as often as necessary. It will be appreciated that for law enforcement applications, the computer  12  may have access to databases and data repositories that are not available to the general public. In some embodiments, the police station  14  memory storage bank  17  houses the database accessed by the vehicle  10  computer  12 . 
     In addition to receiving regular communications via the receiver  13 , the vehicle computer  12  microprocessor is configured with specific instructions to be carried out upon receipt of certain communications, such as Amber alerts, Silver alerts, etc., (via the communication network  18 ) from the police station  14  or other designated agencies or systems, such as the FBI, DEA, ATF, etc. For example, law enforcement agencies often issue Be on Look Out (“BOLO”) alerts to bring to the attention of law enforcement officers key information regarding an occurrence or activity of high importance. Such alerts typically include a description with some known details and facts relating to a suspect or an item or event of interest. The officer who receives the BOLO alert is intended to keep an eye out for the suspect or item of interest by continually or periodically scanning his environment for the particular descriptive details of the suspect/item identified in the alert. 
     The present disclosure provides the officer the means to leverage technology to perform this continual monitoring task. Upon receipt of such alerts, the computer  12  microprocessor activates the camera device  16  (if not already activated) to start collecting information and processing the captured image data to determine whether the specific content identified in the alert is present in the captured image data. The computer  12  microprocessor is configured to search the captured image data for the presence of the designated content according to the received alert or communication. For example, the designated content may include information such as: a geographical parameter (e.g. GPS coordinate), location data (street designation, historic site, monument, etc.), vehicle type (SUV, truck, sedan, motorcycle, etc.), license plate number(s), particular objects (traffic lights, street signs, etc.), particular shapes (human, animal, etc.), or a person, e.g., with particular characteristics. 
     When an object enters the scene, the computer  12  microprocessor performs analytics on the captured image data using an analytics engine that references the accessed database(s), and the analytics include creating snapshots and character scanning, optical character recognition (OCR), pixel scanning, and shape/pattern recognition analytics to analyze and search the captured data for the presence of images matching the designated content. The analytics software may also analyze a scene, tracking identified objects of interest, for example, a police officer&#39;s movements. For example, if an officer falls and becomes horizontal for a certain amount of predetermined time, the microprocessor can send an alert to police dispatch through the communication network  18  so that dispatch can call via radio or cell phone to check on the fallen officer. If there is no response from the fallen officer in a predetermined amount of time, dispatch can send support to assist in case of a serious issue. The shape/pattern detection analytics may also be used to detect objects already in or coming into the scene, such as a person walking or running, and also to detect the direction of travel of such objects. It may also be used to detect objects or people approaching the officer based on changes in the detected measured distance between the officer and person/object, and based on this analysis, the microprocessor can send an alert to the officer on the scene (e.g., via radio, 3G/4G wireless networks, or Body Worn Camera (BWC) speaker over Wi-Fi or Bluetooth®). Additional features that may be provided by the analytics engine include automatically marking image data if a crash was detected in the background of the scene, such as a vehicle rolling or flipping. Yet another aspect of the shape/pattern detection features provided by the analytics engine is the determination of a weapon threat. The scene can be scanned for the detection of objects such as potential weapon types like guns, knives, etc., being held in a person&#39;s hand or for various threatening stances by a potential adversary such as detecting when the adversary is standing, squatting sideways, running, etc. 
     The detection/analytics capabilities of the disclosed embodiments also include the ability to scan the entire or specified area of a scene for any movement. For example, if an officer is parked somewhere filling out a report and looking down, if the system detects movement an alert sound or message on a display (e.g. the vehicle display) can notify the officer to be aware. With multiple viewing angles, the alerts can also notify the officer which direction the movement came from by using distinct sounds for each direction such as front, rear, right side or left side, voice notification of the direction and/or notification messages on the display. The system can also notify the officer if it is a vehicle, person, or an unknown object and if the object is moving fast or in a threatening manner. Such embodiments may incorporate the camera/microphone unit  16  described below with respect to  FIGS. 6-8 . 
       FIGS. 2-4  depict scenes as processed by the computer  12  microprocessor to detect various shapes and objects according to embodiments of this disclosure.  FIG. 2  depicts the recognition of multiple vehicle shapes (shown in and above bounding boxes)  21  in a video frame  20  of the information captured by a camera device  16 . This data may be used, e.g., to detect a vehicle of interest.  FIG. 3  depicts the recognition of various traffic signal shapes (shown in bounding boxes)  31  in a video frame  30  of the information captured by a camera device  16 . The detection of such real-world features can be used to provide additional location data.  FIG. 4  depicts the recognition of multiple “people” shapes (shown in bounding boxes)  41  in a video frame  40  of the information captured by a camera device  16 . Such data may be used to implement the applications related to officer safety or behavior patterns as disclosed herein. 
     In some embodiments, once the analytics engine detects a match or near match of the designated content in the captured image data, the analytics engine proceeds to another step of further analyzing the data containing the designated content to detect for the presence of one or more designated details or attributes of or associated with the designated content. For example, a communication may be received by the receiver  13  (such as a BOLO, Amber, or Silver alert), designating the content to search for as a car, and the attributes as a silver Audi A6 sedan. In this case, the analytics engine will scan and search the captured image data for a match of the descriptor, i.e., the car. If the analytics engine detects the presence of a car in the captured image data, the data is then further analyzed to determine if the designated attributes (i.e., vehicle make —Audi, vehicle model—A6, color—silver, vehicle type—sedan) are present in the data. Other possible designated attributes that may be provided in a communication or alert include, for example: state identifiers (e.g., license plate numbers, characters, emblems, mottos, etc.). In some embodiments, the computer  12  microprocessor continually writes all metadata/attribute information associated with the detected designated content to a text or XML file. It will be appreciated that the designated content descriptors and associated designated attributes may comprise an unlimited variety of items and descriptors, as exist in the real world. The embodiments of this disclosure are not to be limited to any specific content or attribute of such content. 
     In some embodiments, the analysis further includes the determination of a confidence level or criterion for the designated attribute(s). Modern processors provide the ability for high-speed analysis of vast amounts of data. Physical dimensions and parameters of real-world objects represent factual data that can be mathematically measured, analyzed, and compared. For example, the length, width, and height of a vehicle of a given make and model represents factual data. In some embodiments, the analytics engine analysis of the collected data entails a breakdown of the captured images into data points or pixels that are then analyzed to determine respective spacing and dimensions, which can then be compared to the real-world parameters in the database library of existing items. For instance, continuing with the silver Audi A6 example, once the analytics engine detects a vehicle in the image data, it then performs further analysis to detect for the color silver based on a pixel hue analysis, it may then continue the analysis to mathematically define the dimensions of the detected vehicle for comparison against the actual Audi A6&#39;s dimension parameters stored in the database. If a match or near match is found between the dimensions of the detected car and one of the A6 models in the library, the engine then calculates a probability factor representing a confidence level for the match and compares that to a criterion for equivalence or matching of the detected object and the object stored in the database. If, for example, the criterion for equivalence has been set (e.g., by a user via the software) at 95% or greater for vehicle data matching parameters and the calculated probability factor equaled or exceeded 95%, the analytics engine would determine a positive result and proceed with triggering an action as described for the disclosed embodiments. 
     Different criteria for equivalence can be set for different items. For example, the criterion of equivalence for an affirmative match result for a license plate number may be set at 55% or better, to allow for instances when only a partial plate number is decipherable from the captured image. In the case of attributes for which there are no standard items (for comparison against the detected item for purposes of determining equivalence) stored in the database, the analytics engine can bypass this database query and perform a character-recognition analysis. However, for law enforcement applications, the database available to officers will likely contain all available information relating to data such as a license plate number. In some embodiments, the criterion of equivalence for an affirmative match result may be based on a probability factor from a combination of analyzed attributes. 
     In some embodiments, the analytics to determine a confidence level or criterion for the designated attribute(s) are based on a deep learning algorithm. The computer  12  may be configured with software providing a deep learning analytics engine. Defined shapes and movement rules, multiple images of vehicle types, make, model, etc., can be input and stored in the deep learning engine at different viewing angles, distances, various lighting conditions, etc. The captured image data can be compared against the engine contents to provide a data output with a percentage of confidence of accuracy for its attributes to trigger an action as described herein. The analytics and rules can be applied to any object (e.g., pedestrians, animals, street signs, etc.). 
     In some embodiments, the analytics for recognition and detection of the designated content is distributed among the vehicle  10  computer  12  and one or more remote computers (e.g. the server  15  in the police station  14 ). In such embodiments, the server  15  may be configured to generate a neural net object model for the vehicle  10  computer  12 . The vehicle  10  computer  12  can also be configured to use a separate neural network to instantly achieve multiple object recognition as described herein. The vehicle  10  computer  12  and the remote computer(s) can communicate and exchange data via the communication network  18 . In yet other embodiments, the vehicle  10  computer  12  and/or the remote computer(s) (e.g. server  15 ) may be configured with artificial intelligence (AI) software providing the system the ability to learn, to further increase the accuracy of object recognition. In some embodiments, the analytics engine is configured to detect unknown objects (e.g. a modified vehicle). This data can be locally stored for later upload or immediately transmitted to another location (e.g. to server  15 ) for verification and/or classification to aid in the training of detection of objects by the detection engine. With AI implementations, this type of classification can be done in or near real-time on the edge device such as an in-car video unit or a wearable device such as a body worn camera. In this description, an “edge” device generally refers to a device used or located at a point of interest. Thus, for the disclosed embodiments, an edge device is considered an on-scene device. It will be appreciated by those skilled in the art that embodiments of this disclosure may be implemented using conventional software platforms and coding configured to perform the techniques as disclosed herein. 
     Once the analytics engine determines that the designated attribute(s) is/are present in the captured image data, the microprocessor triggers an action. The triggered action may include:
         (i) Providing an alert—The officer may be given a visual and/or audible alert on his vehicle display that a positive match has been detected. An alert may also be sent via the communication network to the police station or other locations. Alert notifications and/or images and metadata may also be sent to any other vehicle or officer that may be nearby or heading in the general direction of the detected object of interest.   (ii) Displaying an image—A video or still image of the detected content may be displayed on the vehicle display. A snapshot (such as video frame  20 ,  30  or  40 ) can be displayed, highlighting or putting a bounding box (such as  21 ,  31 , or  41 ) around the object detected, and its movements can be tracked on the display in real-time or after the fact. Captured image data may also be transmitted via the communication network for viewing on a smart phone, tablet, PC, or the like (e.g., of another officer in another location in the field or at a remote location, e.g., at central headquarters).   (iii) Activating data recording—An onboard storage device may start saving the information being captured by the camera device. Other camera devices in the vehicle can be triggered to start recording. The captured information may also be transmitted concurrently via the communication network to be stored at the police station or another location. The triggered action may also include the activation of other camera devices worn by officers or within other law enforcement vehicles within a set perimeter of the detected item of interest.   (iv) Communicating information relating to the detected content—The captured information (e.g., data files and/or associated metadata files) may be transmitted via the communication network to the police station or other locations (e.g., other police officers in the area). Information such as location (latitude/longitude), direction of travel (e.g., of a vehicle of interest), speed and time of the scan, a snapshot, shape detection attributes, etc., can be transmitted to police dispatch or other remote location. For example, associated metadata and/or a snapshot can be transmitted to a remote location or to any or all other computer devices or members/users in the group or to a dispatch/command center.   (v) Activating a hardware function—In addition to or instead of the activation of data recording (iii, above), equipment or systems may be activated depending on the detected content (e.g., automatic activation of the vehicle&#39;s light bar if a crash scene is detected). Live video/audio streaming may also be activated.   (vi) Activating a software function—A voice-to-text program may be activated to produce a text file of audio captured in association with the captured video. A voice keyword-based program may be activated to trigger other actions (e.g., if a key word is detected such as “gun”, “shots fired”, or “kill”, video can be marked and sent with an alert to the police station or other location or device to inform other officers).   (vii) Extracting and storing content of the obtained information—Other law enforcement agencies or designated systems may be granted access, via the communication network, to download and store the information captured by the camera device.   (viii) Production of a text file—In some embodiments, audio captured in association with the captured video by a mobile audio recorder in the vehicle or worn by the officer can be converted to text (in real-time or from recorded data). The conversion process can output a separate text file for viewing on the vehicle display, and may also transmit the data via the communication network for viewing on a smart phone, tablet, PC, or at a remote location. One use of the text file can be for performing a keyword search during an active or previously recorded event. In some embodiments where video and metadata is continually recorded, all objects or specifically defined objects can be scanned and the metadata associated with the detected objects of interest can be written to a text/xml file or other small database. Metadata captured in the image data or associated with the video data may also be scanned separately, in real-time or from prerecorded metadata. In some embodiments, the vehicle  10  computer  12  may also be configured to mark start and end points in the captured image data associated with scanned metadata. In other embodiments, the vehicle  10  computer  12  may also be configured to isolate images or produce snapshots or video clips associated with scanned metadata. All of this data can be stored locally for a predetermined time or on a FIFO basis; it can also be uploaded to a remote location continually or in configurable intervals and packet sizes. This data can also be stored on a remote server (as described below with reference to a Global Data Access System).       

       FIG. 5  depicts a processing flow chart for an analytics engine  50  embodiment in accordance with this disclosure. As described above, the receiver  13  in the vehicle  10  receives the communication indicating the designated content to search for, along with the known attribute(s) associated with the designated content. At module  52 , the vehicle computer  12  processes the instructions and the microprocessor commences the scanning analysis. At module  54 , the analysis entails a data feed  56  of the image data from the linked camera device(s)  16  to the computer  12  microprocessor. The data feed  56  from the camera device(s)  16  to the computer  12  may be wireless or via cabling (e.g. using a wired onboard vehicle camera). In some embodiments, the computer  12  may be configured to automatically activate the linked mobile camera device(s)  16  to start collecting image data, if the device(s) is/are not already activated. Module  54  also comprises linking with a database  58  to perform the analysis of the collected image data. As previously described, the database  58  may be resident in the vehicle  10  computer  12  or remotely located (e.g. the police station  14  memory storage bank  17 ) and accessed via the communication network  18 . At module  60 , the analysis continues with a scan of the image data captured by the camera device(s)  16  to detect for the presence of the designated content. If the designated content is not detected in the captured image data, module  60  entails a return to module  54  and the analysis continues with the scanning of additional image data collected by the camera device(s)  16 . If the designated content is detected, the flow proceeds with analysis at module  62 , where the image data containing the detected designated content is further analyzed to determine if the data comprises the designated attribute(s) conveyed in the communication. If the designated attribute(s) is/are not detected, module  62  entails a return to module  54  and the analysis continues with the scanning of additional image data collected by the camera device(s). If the designated attribute(s) is/are detected, the flow proceeds directly to module  66 , or in some embodiments to an additional (optional) module  64 . If proceeding directly from module  62  to module  66 , the computer  12  microprocessor triggers an action as previously described. With embodiments comprising optional module  64 , the image data containing the detected designated attribute(s) is further analyzed to determine if the attribute(s) meets the designated criterion, as described above. If the attribute(s) meets the designated criterion, the flow proceeds to module  66  and an action is triggered. If the attribute(s) does not meet the designated criterion, module  64  entails a return to module  54  and the analysis continues with the scanning of additional image data collected by the camera device(s)  16 . The computer  12  can process multiple searches relating to multiple incoming communications, performing the respective scanning/analysis on the captured image data. The collection and processing of image data may be stopped and started as desired by a user (e.g. an officer in the vehicle) entering a command (e.g., a voice command or a command that is typed/keyed/entered by touchpad) or pushing a button on the computer  12  or on a BWC  29 , or by another incoming communication (e.g. from the alert dispatching source) instructing the computer  12  to cancel or start/resume the particular search/analysis. When an alert is cleared or cancelled, the computer  12  resumes scanning all available image data captured in the scene until another communication is received to trigger another action (e.g. another alert) or the officer manually enters a different instruction. The computer  12  may also be configured to automatically clear an alert after running a scan without a match for a predetermined time or when the vehicle  10  is housed or turned off (e.g. when a patrol car is parked at the police station at the end of a shift). It will be appreciated by those skilled in the art that embodiments of the analytics engine disclosed herein may be implemented using any suitable computer platforms and coding as known in the art. 
     A benefit of the functionality provided by the disclosed embodiments is that the camera device and detection/analytics engine may find an object or person of interest that a police officer didn&#39;t notice. For example, a police officer may be driving down the street when a BOLO is issued for the silver Audi sedan. The officer may be focusing on driving or performing some other activity/task and may not see the item of interest, in this case the disclosed systems can alert multiple officers to be aware of the potential object of interest and thereby improve the chances for detection. This can also increase safety and efficiency for the officer. Officer efficiency may also be improved with embodiments wherein the camera device and detection/analytics engine are configured to detect expired vehicle tags. Once the analytics engine makes such a determination, the microprocessor can trigger an action as described above (e.g., flash an alert on the vehicle display, issue a notice to the police station  14 , record the information as metadata, etc.). Moreover, the disclosed embodiments provide the means to perform the described detection and analytics techniques in real-time, as image data is being captured. 
     Turning to  FIG. 6 , an overhead view of a camera device  16  comprising a spherical camera that may be used with implementations of this disclosure is depicted. The camera device  16  is configured for roof-mounting on a vehicle  10  and provides a 360-degree view.  FIG. 7  depicts the bottom view of the spherical camera device  16 , showing (more fully than in  FIG. 6 ) a series of directional microphones ( 1 - 4 ). With the directional microphones ( 1 - 4 ), the approximate direction  8  of sounds emanating from a sound source  9  can be detected. With the combined camera/microphone unit  16 , the computer  12  microprocessor can be configured to automatically mark points in the captured video data at which specific sounds  5  are detected (as depicted in  FIG. 7 ) and take snapshots  6  and/or create a video event clip  7  of a predetermined length pre- and post-event (as depicted in  FIG. 8 ). In some embodiments, the camera device  16  may be configured to continually record captured video data in a circular or “failsafe” buffer that can be flushed or transferred for storage to memory.  FIG. 9  depicts a schematic flow chart  70  of a spherical camera device  16  as used in combination with the microphones ( 1 - 4 ) to capture and process the detection of gun shots. At step  72 , the audio data captured by the microphones ( 1 - 4 ) is processed to determine the approximate direction of the sound source  74 . At step  76 , the spherical camera device  16  software control adjusts the camera to capture image data in the approximate direction of the sound source. If the audio processing determines that the sound source was a gunshot  78 , a gunshot detection signal is sent to trigger the camera device(s)  16  to start recording and an emergency announcement, or actionable notification, or request, is sent via the communication network to the police station  14  or to other nearby devices (step  80 ). Other embodiments of this disclosure may be implemented with other types of camera devices  16  besides a spherical camera (e.g., using a conventional multi-image sensor camera), to provide the desired image clarity and depth of field. The embodiments of this disclosure may be implemented to incorporate in the described recognition analytics any conventional or future developed sensors providing other types of data besides image/video/audio data (e.g. distance measuring sensors, etc.). 
     In some embodiments, the vehicle  10  computer  12  microprocessor may also be configured with instructions to send out a communication (via the communication network  18 ) to activate the camera devices  16  in other law enforcement vehicles (e.g., in-car video (ICV) units  28 ), and the BWCs  29  worn by officers, within a set range or perimeter of where the object of interest (corresponding to the designated content) was detected, as depicted by the arrows in  FIG. 10 . With this embodiment, all the law enforcement vehicles with camera devices  16  and officers with BWCs  29  on the street within the set range or perimeter are turned into scanners, all activated to search for and capture image data relating to the designated content or object of interest. Other embodiments entail the use of an autonomous vehicle  10  to scan the scene for the object attributes in a received communication (e.g. a BOLO alert) and to send a notification to officers nearby and/or to a police dispatch center. The vehicle  10  can track the detected vehicle or person providing real-time or historical location data updates to the officers responding and/or to a police dispatch center. This could help the officers more accurately pursue the vehicle or person of interest. 
     As previously mentioned, BWCs can be used with implementations of the embodiments of this disclosure. Suitable BWCs include the devices commercially available from COBAN Technologies Inc., in Houston, Tex. (http//www.cobantech.com). The BWCs are worn by officers on patrol. The BWC can be conveniently clipped to the officer&#39;s uniform or body gear as desired. BWCs may also be configured with a microphone to collect audio data. The collected audio data may be transmitted together with the captured image/video and/or metadata to another device (e.g., located in a police car, at a police station, on another police officer, or in the cloud) as described herein. It will be appreciated by those skilled in the art that various conventional BWC devices and storage units may be used to implement embodiments of this disclosure. Similarly, various wireless technologies may also be used to implement the embodiments as known in the art. It will also be appreciated that as technology improves, smaller and lower power camera and transmission devices may become available which may further improve performance and run time. Such devices may easily be integrated into the embodiments of this disclosure. 
     In some embodiments, the vehicle  10  computer  12  may be configured to perform wireless networked or distributed analytics processing. As previously described, in some embodiments the vehicle  10  computer  12  is configured to access an onboard database and perform the disclosed analytics processing as a stand-alone unit. In other embodiments, the vehicle  10  computer  12  may be configured to communicate via the communication network  18  (e.g. using the cloud) with other computers (e.g. remote ICV units  28  and BWCs  29 ) to perform a distributed and shared image data analysis. With reference to  FIG. 10 , the vehicle  10  computer  12  microprocessor may issue a communication to the ICV units  28  in police vehicles and BWCs  29  within a set range, to not only activate the respective camera devices, but also including selected image data information to activate the remote devices to perform a local analysis. For example, if an alert is received by the vehicle  10  computer  12  describing a crime involving suspects in multiple vehicles and the computer detects one of the vehicles, the microprocessor sends a communication (in real-time) to all other units/officers within the set range, including the captured image data or components of the captured image data (e.g. metadata alone; each of video data, audio data, and metadata is considered a component of the captured image data). The ICVs  28  and BWCs  29  receiving the information can then focus on scanning to detect for the other vehicles described in the alert. Similarly, image data captured by the ICVs  28  and BWCs  29  can be relayed back to the vehicle  10  computer  12  via the communication network  18  for hubbed real-time processing. In this manner, the ICVs  28  and BWCs  29  are used to form a virtual net providing real-time processing and communication. The communication network  18  (e.g. the cloud) provides a relay data point for all ICVs  28 , BWCs  29 , vehicles  10 , and any other wirelessly linked devices or systems in the vicinity where events of interest are occurring, as well as a relay data point to or from remote locations. This network provides for vehicle-to-vehicle and system-to-system distributed communications. The communication loop in such embodiments may also include the police station, dispatch headquarters, and other law enforcement agencies. 
     In some embodiments, the ICV  28  is configured to detect and take snapshots, or receive snapshots from a wearable device (e.g. BWC  29 ), of a person&#39;s face to run facial recognition locally or by transmitting the data to a remote server (e.g. server  15 ) for further analytics. This further enhances the BOLO capabilities. For example, a BOLO may include an alert to look for a white male, wearing a black jacket, having an age in the mid-twenties, etc. The detection of attributes is also enhanced, such as detection of approximate age, gender, and race. The use of AI software and other advanced software applications may provide additional benefits. Some embodiments may also be configured to receive video data via transmission such as Real Time Streaming Protocol (RTSP) streaming for detection and analytics of attributes and facial recognition. Some embodiments of this disclosure provide for selective search and export of the captured information. In one such embodiment, an authorized user linked to the computer  12  microprocessor via the communication network  18  (e.g., using a smart phone, laptop computer, tablet, etc.) can analyze the information according to specific criteria established by the user. For example, a user can select or draw an area on a map to display vehicles in a given region, along with their associated data such as specific location data/time/number of recorded events/event type/duration, license plate data, vehicle type, shape, color etc. If an event or specific data is of interest, the user can select an option to send a request to any or all vehicle computers  12  to scan their storage drives, that are continuously recording, for the desired information and send back a response with the search results or to retrieve the designated data with time markers of start and stop points to export video, snapshots, or metadata. This embodiment can be implemented for a local or global application. 
       FIG. 11  depicts another embodiment of this disclosure. A computer server  26  is linked to one or more camera devices  16  (e.g. ICV units  28  and BWCs  29 ) via the communication network  18 . Some embodiments may also incorporate fixed camera devices  16  (e.g. cameras on buildings, light poles, etc.). This embodiment provides for shared Global Data Access and distribution, allowing authorized users  27  to access the server  26  via the communication network  18 . With this embodiment, the server  26  provides a centralized repository for the specific data and information of interest to a user group (e.g. law enforcement agencies). Using such a globally shared server, authorized users  27  can send data of interest to the server  26  for storage (e.g., in local memory or linked data storage). In some embodiments, the server  26  may be partially or entirely implemented as a virtual server in the cloud. Such cloud computing embodiments offer flexibility of scalability. The server  26  is also configured to analyze the data sent to it (encompassing audio, video, metadata, and/or any other available telemetry) by an authorized user in the group, or captured by the linked mobile device(s)  16 , to analyze and search for the presence of designated content and designated attributes in the data, as described herein. The server  26  may also be configured to perform full or partial analytics on the captured image data and the data sent by authorized users. The data stored in the server  26  could remain available for a defined period to allow authorized users access to the data. For example, if a BOLO alert is issued an application on the server  26  can scan the stored data (including stored metadata) and if an attribute of interest is detected the application can generate a request to be sent to the linked camera device  16  that captured the detected image data to mark configurable in (start) and out (stop) points of the recorded video, audio, metadata, etc. The marked image data can also be exported and/or uploaded or a live stream can be started if the camera device is still near the location where it originally captured the data. If high importance image data is detected, the server  26  can send alerts or emails to users of the Global Data Access System. For example, if an agency (police department, FBI, DEA, ATF, etc.) is looking for a vehicle having a specific color and make or license plate, an authorized user can enter this information as designated content/attributes in to the Global Data Access System server  26  (e.g., using a smartphone, desktop computer, laptop computer, or tablet device). The server  26  then sends a command, via the communication network  18 , to any linked camera device  16  to analyze its recorded image data to search for matching audio, video, and/or metadata, as the case may be. If a match or near match is found, the device that found the match or near match notifies the server  26 , and the detected matching or near matching image data, or components thereof (e.g. metadata), are then uploaded and saved (e.g. to local memory) by the server  26 . In some embodiments, the server  26  may also be configured to send a notice (e.g. via email) to the searching agency or others, regarding the detected data. In some embodiments, the server  26  may be configured to perform the image data analysis on real-time image data being collected by any linked camera devices. The server  26  may also be configured to analyze the data stored in memory and/or captured by the camera device(s)  16  according to criteria established by an authorized user  27  having access to the server  26  via the communication network  18  (e.g. in response to a BOLO alert). 
       FIG. 12  is a flow chart depicting a method  100  according to an embodiment of this disclosure. At step  110 , a wireless communication is received with a receiver, indicating designated content to search for in image data. The communication may be conveyed via the communication network  18 . At step  120 , image data is obtained using at least one camera device  16  (e.g., ICV  28 , BWC  29 ) configured to capture image data. At step  130 , using a microprocessor linked to the receiver and the at least one camera device, (a) the obtained image data is analyzed to determine if it contains the designated content and (b) obtained image data containing the designated content is analyzed to determine if it contains at least one designated attribute associated with the designated content. This may be implemented using an analytics engine and processing as described herein. At step  140 , an action is triggered by the microprocessor if the at least one designated attribute is detected in the obtained image data containing the designated content. This entails any of the actions that may be triggered by the microprocessor as described herein. 
       FIG. 13  is a flow chart depicting a method  200  according to an embodiment of this disclosure. At step  210 , a computer server is linked via a communication network to at least one camera device  16  (e.g., ICVs  28 , BWCs  29 ) configured to capture image data. At step  220 , using the server, (a) image data captured by the at least one camera device is analyzed for the presence of designated content and (b) captured image data containing the designated content is analyzed for the presence of at least one designated attribute associated with the designated content. This may be implemented using an analytics engine and processing as described herein. At step  230 , using the server, the captured image data or components of the captured image data, containing the at least one designated attribute, are uploaded for storage. At step  240 , using the server, a notice relating to the captured image data or to the components of the captured image data, is sent via the communication network. 
       FIG. 14  depicts disclosure a system  300  including a vehicle  310 , wherein the vehicle  310  includes a number of components. System  300  features at least one camera device  316  configured to capture image data. Camera device  316  is mounted to vehicle  310 . System  300  also comprises at least one sensor configured to provide vehicle telemetry data to computer  312 . The at least one sensor may include, but is not limited to, an accelerometer  303 , a speed sensor  304 , and/or a GPS device  305  (e.g. antenna). The sensors transmit telemetry data by communication links  306 ,  308  and  309  (which may be wired or wireless) to a computer  312 . Computer  312  includes a microprocessor  311 , an internal memory  313  and an analytics engine as described above. While  FIG. 14  depicts the above-referenced components mounted separately and apart from one another throughout vehicle  310 , they could also share a single housing mounted in a single location on vehicle  310 . Furthermore, one of ordinary skill in the art would understand that one could mount the various components in alternate locations on vehicle  310 . The internal memory  313  of computer  312  is configured to store designated content, designated events, triggers, vehicle telemetry, and other data. The microprocessor  311  of computer  312  is configured with instructions to analyze the captured image data and telemetry data for the presence of at least one designated event. As discussed, below, non-limiting examples of designated events may include speeding, driving too slowly, aggressive driving, reckless driving, swerving, collisions, and rollover accidents. The microprocessor  311  of the computer  312  can be further configured with instructions to trigger an action if the designated event is detected based on the captured image data or telemetry data. 
     As discussed, computer  312  may be configured to store the telemetry data in internal memory  313 . The telemetry data may also be transferred to a cloud sever. The telemetry data can be multiplexed and synchronized with the captured image data (or other data) for later analysis, evidence gathering, evaluation, and training. The telemetry data may also be stored as metadata for the captured image data of camera device  316 . Preferably, the process of sampling, storing, and analyzing the telemetry data would be initiated automatically to reduce the workload on the LEO. For example, computer  312  may begin sampling, storing, and analyzing the telemetry data when vehicle  310  is started if the law enforcement agency intends to monitor the driving behavior of the LEO at all times. Alternatively, the computer  312  may begin sampling, storing, and analyzing the telemetry data when the emergency light bar  307  is activated if the law enforcement agency intends to monitor the driving behavior of the LEO when the LEO is responding to call or engaged in a pursuit. Alternatively, the computer  312  may begin sampling, storing, and analyzing the telemetry data when motion of the vehicle is detected or the vehicle reaches a particular speed. Additionally, the computer  312  can bookmark the activation of the emergency light bar  307  within the metadata for the captured image data. 
     According to one aspect of this disclosure, system  300  may be configured to detect the speed of vehicle  310 . The computer  312  may determine the speed of vehicle  310  in several different ways. For example, the computer  312  may sample the speed sensor  304 . Speed sensor  304  may be located on the wheel, within the wheel hubs, on brake components, within the transmission, within the torque converter, within the differential, on the flywheel or other rotating components of vehicle  310 . The speed sensor can be of a magnetic type (e.g. Hall Effect, or Reed Switch) or an optical speed sensor. The computer  312  may also sample the telemetry data from GPS device  305  at a known interval and calculate the change in position of the vehicle  310  over a known time interval using data obtained from the GPS device  305 . The computer  312  may also sample the telemetry data from accelerometer  303  at a known interval. The computer  312  may then calculate the vehicle&#39;s speed based upon its acceleration over a period of time. 
     The computer  312  may also employ the above-referenced analytics engine to analyze the image data captured by the camera device  316 . As discussed, above, the captured image data can be analyzed to determine physical dimensions and parameters of observed objects, which can then be compared to known dimensions and parameters in the database library. For example, the analytics engine can recognize known objects (i.e. with known dimensions) in the captured image data. The image data can be sampled at a known rate. The computer  312  can calculate the change in size of the known objects relative to the entire frame of the image. In other words, the rate at which an object of known size appears to increase (or decrease) can be used to calculate the relative speed of the vehicle  310  to the known object. Thus, this is one way in which the computer  312  can determine the speed of vehicle  310  without use of sensors (speed sensor, accelerometer, GPS device, etc.). 
       FIG. 15  depicts a scene as processed by the computer  312  microprocessor  311  to detect various shapes and objects according to embodiments of this disclosure. The computer  312  recognizes shapes (shown in bounding boxes) in a video frame  320  of the image data captured by camera device  316 . Non-limiting examples of the shapes identified by the computer  312  include cyclists (bounding box  321 ), other vehicles (bounding boxes  322 ), pedestrians (bounding boxes  323 ), and traffic signs (bounding box  324 ). 
     The computer  312  can also determine the applicable speed limit. The computer  312  can determine the applicable speed limit from one or more sources. For example, the internal storage for the computer  312  can store a database which relates location to the applicable speed limit. Sampling the data from the GPS device  305 , computer  312  can look up the corresponding applicable speed limit from the database in its internal storage. In some cases, the GPS device  305  may receive explicit local speed limit data. As depicted in  FIG. 15  the computer  312  may also derive the applicable speed limit from recognition of a speed limit sign (bounding box  324 ) from the image data captured by the camera device  316 . In this case, the analytics engine can recognize speed limit signs in the captured image data in the same way the computer recognizes license plates as discussed, above. Specifically, computer  312  can employ OCR, pixel scanning, and shape/pattern recognition analytics to analyze and search the captured data for the applicable speed limit. The computer  312  may also employ a combination of data sources to determine the applicable speed limit. For example, the computer  312  may default to the stored speed limit where posted speed limit signs are not present. Where posted speed limit signs are present, the computer  312  may determine the applicable speed limit from those posted speed signs. Having determined the speed of the vehicle  310  and the applicable speed limit, the computer can compare the two in order to detect the presence of one or more designated events. A speed of the vehicle  310  above the applicable speed limit would indicate the designated event of speeding. The computer  312  can also be configured with instructions to detect when vehicle  310  is driving below the applicable speed limit by a predetermined margin. This may indicate a distracted, fatigued, or intoxicated driver. 
     System  300  may be configured to monitor different designated events indicative of disfavored driving behaviors. For example, the system can detect when vehicle  310  fails to stop at a stop sign.  FIG. 16  depicts a scene when vehicle  310  approaches stop sign  331 . The analytics engine can recognize stop sign  331  in the captured image data (i.e. bounding box  330 ). The dimensions of the stop sign  331  as recognized by computer  312  are represented by “X” and “Y” of the bounding box  330  in  FIG. 16 . The dimensions of the video frame  320  are represented by “A” and “B” in  FIG. 16 . As the vehicle  310  approaches the stop sign  331 , the dimensions X and Y increase relative to the dimensions A and B of the video frame  320 . In other words, the stop sign  331  grows larger in the video frame  320 . 
     The computer  312  may sample the telemetry data from the vehicle  310  and/or analyze the image data captured by the camera  316  to determine whether the vehicle  310  stopped at the stop sign  331 . For example, the computer  312  may sample the captured image data at a known rate. Having identified stop sign  331  represented by bounding box  330  in the video frame  320 , the computer  312  would calculate the dimensions X and Y for each sampling of the video frame  320 . Where two or more samplings of the video frame  320  are equivalent, the computer  312  could determine that the vehicle  310  was not moving relative to the stop sign  331  (which is stationary) for the period of time between the video samples. In other words, two or more samplings of the video frame  320  with constant dimensions X and Y are indicative of a stationary vehicle. In contrast, where dimensions X and Y continuously increase until the stop sign  331  disappears out of the video frame  320  (i.e. X and Y are not constant for two or more samplings of the video frame  320 ), the computer  312  may determine that the vehicle  310  did not come to a complete stop. Alternatively, the computer  312  can calculate the change in size of known objects (e.g. the stop sign  331 ) relative to the entire frame of the image to calculate the relative speed of the vehicle  310  to the known object. Analyzing this data, the computer  312  can determine if vehicle  310  has come to a complete stop (i.e. speed=0) within a predetermined proximity of a stop sign. The computer  312  may also sample the telemetry from the vehicle&#39;s speed sensor  304 , accelerometer  303 , and/or GPS device  305  to determine the vehicle&#39;s speed. 
     Similarly, the system  300  may be configured to detect when the vehicle  310  fails to stop at a stop light.  FIG. 17  depicts a scene when the vehicle  310  approaches a traffic light  341 . The analytics engine can recognize the traffic light  341  in the captured image data (i.e. bounding box  340 ). The dimensions of the computer&#39;s recognition of the traffic light  341  are represented by “X” and “Y” of bounding box  330  in  FIG. 17 . The dimensions of the video frame  320  are represented by “A” and “B” in  FIG. 17 . As the vehicle  310  approaches the traffic light  341 , the dimensions X and Y increase relative to the dimensions A and B of the video frame  320 . In other words, the stop light  341  grows larger in the video frame  320 . 
     The computer  312  may sample the telemetry data from the vehicle  310  and/or analyze the image data captured by the camera  316  to determine whether the vehicle  310  stopped at the traffic light  341 . For example, the computer  312  may sample the captured image data at a known rate. Having identified traffic light  341  represented by bounding box  340  in video frame  320 , the computer  312  would calculate the dimensions X and Y for each sampling of the video frames sampled. Where two or more samplings of the video frame  320  are equivalent, the computer  312  could determine that the vehicle  310  was not moving relative to the traffic light  341  (which is stationary) for the period of time between the video samples. In other words, two or more samplings of the video frame  320  with constant dimensions X and Y are indicative of a stationary vehicle. In contrast, where (i) the computer  312  recognizes the traffic light  341  as red, and (ii) dimensions X and Y continuously increase until the traffic light  341  disappears out of video frame  320  (i.e. X and Y are not constant for two or more samplings of the video frame  320 ), the computer  312  may determine that the vehicle  310  did not come to a complete stop. Alternatively, the computer  312  can calculate the change in size of the known objects (e.g. the traffic light) relative to the entire frame of the image to calculate the relative speed of the vehicle  310  to the known object. Analyzing this data, the computer  312  can determine if the vehicle  310  has come to a complete stop (i.e. speed=0) within a predetermined proximity of the traffic light  341 . The computer  312  may also sample the telemetry from the vehicle&#39;s speed sensor  304 , accelerometer  303 , and/or GPS device  305  to determine the vehicle&#39;s speed. 
     In another embodiment the computer  312  samples telemetry from the GPS device  305 . The computer  312  may determine the vehicle&#39;s position at various times determined by the sample rate. The computer  312  can calculate the change in position relative to the sample period to determine the vehicle&#39;s speed. Similarly, the computer  312  can calculate the acceleration (i.e. change in speed over time). In another embodiment, the analytics engine can recognize known fixed (i.e. stationary) objects (e.g. signage) in the captured image data. The image data can be sampled at a known rate. The computer  312  can calculate the change in size of the known objects relative to the entire video frame as discussed above. In other words, the rate at which fixed objects of known size appear to increase (or decrease) can be used to calculate the relative speed of the vehicle  310  to the known fixed objects. With this data, the computer  312  can calculate the acceleration of the vehicle  310 . This calculated acceleration may also be used to determine whether the driver is driving aggressively. 
     In another aspect of the disclosure, the system  300  can detect a near collision with another vehicle, pedestrian or object. As discussed, above, physical dimensions and parameters of real-world objects can be analyzed to determine respective spacing and dimensions, which can then be compared to the real-world parameters in the database library of known items and dimensions. The analytics engine can recognize known objects (i.e. with known dimensions) in the captured image data. The image data can be sampled at a known rate. The computer  312  can calculate the change in size of the known objects relative to the entire video frame of the captured image. The rate at which an object of known size appears to increase in the image frame can be used to calculate the relative closing speed of the vehicle  310  to the known object. Where the known object is fixed, the analytics engine can calculate the absolute speed and acceleration of the vehicle using the techniques disclosed above. 
     System  300  can sample the vehicle telemetry and store the telemetry data on internal storage and/or upload the telemetry data to a cloud server. The computer  312  may store separate telemetry files from each sensor, or it may multiplex the telemetry data from multiple sensors into a single file. The telemetry data may be stored in a separate file from the video images captured by the camera device  316  or the telemetry data may be stored in metadata for the video file of the video images captured by the camera device  316 . The computer  312  may transmit any of the captured image data, telemetry data and metadata via the communication network for viewing on a smart phone, tablet, PC, or at a remote location. In some embodiments, the vehicle  310  computer  312  may also be configured to mark start and end points in the captured image data, telemetry data and/or metadata. In other embodiments, the vehicle  310  computer  312  may also be configured to isolate images or produce snapshots or video clips associated with the metadata. The computer  312  may also be configured to isolate portions of the telemetry data associated with the metadata. All of this data can be stored locally for a predetermined time or on a FIFO basis; it can also be uploaded to a remote location continually or in configurable intervals and packet sizes. 
     Other driving behaviors can be monitored as well. The computer  312  may be configured to monitor telemetry data from the accelerometer  303 .  FIG. 18  depicts the six vectors which can be measured by a 3-axis accelerometer mounted to the vehicle  310 . A vehicle&#39;s longitudinal acceleration is depicted as +/−Y values. A positive value (+Y) represents the vehicle&#39;s acceleration. A negative value (−Y) represents the vehicle&#39;s deceleration. A vehicle&#39;s lateral acceleration is depicted as +/−X values. A negative value (−X) represents the vehicle&#39;s change in direction to the left. A positive value (+X) represents the vehicle&#39;s change in direction to the right. A vehicle&#39;s vertical acceleration is depicted as +/−Z values. A negative value (−Z) represents the vehicle&#39;s change in direction down. A positive value (+Z) represents the vehicle&#39;s change in direction up. It should be noted that this disclosure refers to acceleration values as a multiple of the acceleration due to gravity at the Earth&#39;s surface, which this disclosure identifies with the variable “G” or “Gs” (e.g. 2 Gs=2×9.80665 m/s2). 
     Having sampled the vehicle telemetry and captured the image data, the computer  312  can also be configured with software instructions to determine if the driver&#39;s behavior falls within or outside one or more sets of predetermined parameters. For example, the computer  312  can be configured with software instructions to determine if the values of accelerometer  303  (in any direction) exceed one or more predetermined thresholds. While this example is offered in connection with the sampling telemetry data from the accelerometer  303 , the computer  312  can also be configured with software instructions to determine if other telemetry data (e.g. speed, etc.) falls within or outside one or more sets of predetermined parameters. 
     Further in regard to acceleration, the computer  312  may, for example, be configured to monitor longitudinal acceleration and deceleration (i.e. the vehicle&#39;s acceleration or deceleration in the forward-backward direction) by sampling the Y-values of accelerometer  303 . Additionally, the computer  312  can be configured with software instructions to determine if the vehicle&#39;s acceleration or deceleration is excessive—i.e. above one or more predetermined thresholds. Additionally, the computer  312  can monitor lateral acceleration and deceleration (i.e. the vehicle&#39;s acceleration in the left or right direction) by sampling the X-values of accelerometer  303 . Excessive lateral acceleration is indicative of swift lane changes or swerving. Excessive acceleration in either the X or Y values may indicate disfavored driving behavior such as aggressive driving, distracted driving, fatigue, or a driver under the influence. Significant deceleration would be indicative of a collision. 
     For example, a high performance vehicle may experience maximum longitudinal acceleration of 0.8 Gs for the Y-values in  FIG. 18  when accelerating. The high performance vehicle may experience maximum longitudinal deceleration of −1.3 Gs when decelerating. Maneuvering a high performance vehicle around a circle with a 300 foot diameter may yield maximum lateral acceleration of 1.1 Gs for the X-values in  FIG. 18 . 
     A law enforcement agency may determine that safe, attentive driving would be consistent with: (i) maximum longitudinal acceleration of 0.5 Gs for the Y-values when accelerating, (ii) maximum longitudinal deceleration of −0.8 Gs when decelerating, and/or (iii) maximum lateral acceleration of +/−0.7 Gs for the X-values. As discussed, below, computer  312  could trigger one or more actions when these values are above these thresholds. 
     In another embodiment, a law enforcement agency may also determine the interests of law enforcement (e.g. suspect apprehension or responding to an emergency call) might militate in favor of more aggressive driving. Therefore, the computer  312  can be configured with software instructions to determine if the vehicle&#39;s longitudinal or lateral acceleration exceed a second predetermined set of threshold values. This may be so because while the interests of law enforcement may justify more aggressive driving by an LEO, the law enforcement agency may also wish to trigger one or more events based upon this second predetermined set of threshold values for driving. For example, a law enforcement agency may determine that safely responding to an emergency call would be consistent with: (i) maximum longitudinal acceleration of 0.8 Gs for the Y-values when accelerating, (ii) maximum longitudinal deceleration of −1.0 G when decelerating, and/or (iii) maximum lateral acceleration of +/−0.9 Gs for the X-values. It should be noted that the values discussed in connection with the threshold values are provided as non-limiting examples. Law enforcement agencies may employ different values based upon their own judgments. 
     The computer  312  may also be configured with software instructions to determine whether to bypass the determination of whether vehicle  310  is exceeding the first predetermined threshold values. This determination may be based on one or more inputs such as the activation of the emergency light bar  307  of vehicle  310 , a radio communication, a separate control operated by the LEO, or the vehicle&#39;s telemetry data. As a non-limiting example, the computer  312  could be configured with software instructions to determine whether emergency light bar  307  of the vehicle  310  has been activated. The activation of the emergency light bar  307  of the vehicle  310  could be used to bypass the determination of whether the vehicle  310  is exceeding the first predetermined threshold values. This may be useful where the LEO is responding to an emergency call and it is expected that the LEO&#39;s driving behavior will exceed the first predetermined threshold values. 
     In another embodiment, the computer  312  may trigger the activation of the emergency light bar  307  of the vehicle  310  if the first predetermined threshold values have been exceeded. This may be useful to reduce the LEO&#39;s workload in the case of an emergency call or pursuit. If there is no emergency call or pursuit then it could serve as an alert to the LEO that the LEO has exceeded the first predetermined threshold values. 
     In yet another embodiment, a law enforcement agency may determine that the interests of law enforcement justify some level of heightened risk from the LEO&#39;s more aggressive driving. The law enforcement agency may still seek to limit the risk to the LEO and the public. Therefore, the computer  312  can be configured with software instructions to determine if the vehicle&#39;s longitudinal or lateral acceleration exceed a third predetermined set of threshold values. While the interests of law enforcement may justify more aggressive driving by an LEO, the law enforcement agency may also wish to trigger one or more events based upon this third predetermined set of threshold values for driving. For example, a law enforcement agency may determine that danger to the LEO and the public are consistent with: (i) maximum longitudinal acceleration of 0.8 Gs for the Y-values when accelerating, (ii) maximum longitudinal deceleration of −1.1 G when decelerating, and/or (iii) maximum lateral acceleration of +/−1.1 Gs for the X-values. 
     A similar system of tiered threshold values can also be applied to other types of data. As another non-limiting example, the computer  312  may be configured with software instructions to determine the speed of the vehicle  310 . Rather than comparing the vehicle&#39;s speed against a first predetermined threshold value, the computer  312  could compare the vehicle&#39;s speed against a variable value—e.g. the speed limit. Alternatively, the computer  312  could compare the vehicle&#39;s speed against a predetermined range of values around the speed limit—e.g. between (i) the speed limit−10 mph and (ii) the speed limit+5 mph. Alternatively, the computer  312  could compare the vehicle&#39;s speed against a predetermined range bounded by the speed limit—e.g. between the speed limit and 10 mph less than the speed limit. Once again, the values provided are for illustrative purposes only and could be determined based on the judgment of each law enforcement agency. Collectively, this disclosure refers to these thresholds (limits of the ranges) as “speed thresholds.” Conceptually, an LEO speeding may be a disfavored practice by the law enforcement agency, and driving too slowly may be indicative of another disfavored driving practice such as distracted driving, driving while fatigued, or intoxication. Where the LEO is responding to an emergency call, however, it may be expected that the LEO&#39;s driving behavior will exceed the posted speed limit. A law enforcement agency may determine that the interests of law enforcement justify some level of heightened risk from the LEO&#39;s more aggressive driving, but the law enforcement agency may still seek to limit the risk to the LEO and the public. Therefore, computer  312  can be configured with software instructions to determine if the vehicle&#39;s speed is outside a second speed threshold. Where the LEO&#39;s driving behavior exceeds this second speed threshold, computer  312  may trigger one or more actions. Non-limiting examples of such actions are discussed, below, and may include providing an alert to the driver, sending an alert to dispatch, or activating emergency light bar  307  or siren. It may be desirable to take additional actions where the LEO&#39;s speed exceeds a third speed threshold. Non-limiting examples of such actions are discussed, below, and may include intervening into the vehicle&#39;s dynamics (e.g. to apply the brakes), limit the LEO&#39;s throttle demand, or alert the LEO to terminate a pursuit of a suspect vehicle. This tiered approach is consistent with the dichotomy discussed, above, where the interests of law enforcement conflict with the interests of public safety and the safety of the LEO. The tiered approach may reflect a law enforcement agency&#39;s judgment that the LEO should be provided discretion to respond to an emergency, while placing boundaries on that discretion to protect the public and the LEO. 
     Collisions and rollovers can also be detected with system  300  by sampling telemetry data from the accelerometer  303 . During a collision the magnitude of the telemetry data from accelerometer  303  is much higher than the magnitude of the telemetry data from the accelerometer  303  discussed in connection with aggressive driving. For example, where the maximum longitudinal deceleration experienced by a vehicle may be approximately −1.3 Gs, a vehicle involved in a collision would experience much greater values of deceleration. For example, a vehicle stopping from a speed of 30 miles per hour in 1 foot would experience approximately −30 Gs. This type of rapid deceleration is indicative of a collision. Similarly, a vehicle&#39;s vertical acceleration (depicted as +/−Z values in  FIG. 18 ) should be relatively low and only fluctuate as the vehicle experiences body roll or as the vehicle traverses uneven ground. The vehicle&#39;s vertical acceleration would increase sharply if the vehicle were involved in a rollover. In either case, the computer  312  may also be configured with software instructions to determine if there is a spike in acceleration above a predetermined threshold which is consistent with a collision or a rollover. The computer  312  may also be configured with instructions to trigger one or more actions in the event a collision or rollover is detected by the computer  312 . A non-limiting example may include sending an emergency communication notifying dispatch of the collision or rollover. This could be beneficial, particularly where the LEO is not conscious, injured, or otherwise unable to request medical attention. 
     Still other driving behaviors may also be detected. As one example, it may be determined whether or not a driver of a given vehicle is maintaining a safe distance from a vehicle in front of him (put in other words, whether or not the driver is driving dangerously close to a vehicle in front of him). Similarly, it may be determined whether or not a driver of a given vehicle is maintaining a safe distance from a vehicle in to his side (put in other words, whether or not the driver is passing dangerously close to a vehicle on his side), e.g., a moving or parked vehicle. For the purposes of this disclosure, failure to maintain a safe distance is deemed an instance of aggressive or reckless driving. The determinations as to whether a safe distance is being maintained may be made based on captured image data of the two vehicles and/or data from sensors, such as GPS data (e.g., pertaining to the positions of the two vehicles), speed data, and acceleration data. The determinations may be made based on preassigned values of safe distances, which values have been inputted into the analytics engine for use in making the determinations. For example, a preassigned value of a safe distance between two vehicles may be 2 seconds (based on the speed of the vehicles) or a certain number of feet or vehicle lengths. One of ordinary skill in the art will understand how the analytics engine may make such determinations, in view of the description herein of other determinations made by the analytics engine. 
     Another driving behavior that may be detected is (repeated) hard braking of a vehicle. For the purposes of this disclosure, (repeated) hard braking of a vehicle is deemed an instance of aggressive or reckless driving. One of ordinary skill in the art will understand how the analytics engine may detect this behavior, e.g., based on acceleration (deceleration) and/or speed data in view of the description herein of other determinations made by the analytics engine. Another driving behavior that may be detected is improper crossing of a line marked on the roadway, e.g., crossing a double yellow line (which it is forbidden to cross), or repeated crossing of a white lane line (which is allowed, but repeated crossing of it may indicate aggressive, reckless, or intoxicated driving). For the purposes of this disclosure, improper crossing of a line marked on the roadway is deemed an instance of aggressive or reckless driving. One of ordinary skill in the art will understand how the analytics engine may detect this behavior, e.g., based on captured image data and/or sensor data, whether from a GPS device or other sensor, in view of the description herein of other determinations made by the analytics engine. 
     Having discussed the detection of the occurrence of various designated events, which the analytics engine is capable of performing, we turn to examples of various actions which may be triggered in response to such events. Once the analytics engine determines that one of the above-referenced events has occurred, the computer  312  may trigger one or more of the actions such as those discussed above. For example, but not by way of limitation, the triggered action may include:
         (i) Providing an alert—The officer may be given a visual and/or audible alert on his vehicle display that the computer  312  has detected one or more of the above-referenced events (e.g. speeding, failing to stop at a stop sign or red light, a collision or near collision, aggressive acceleration, deceleration or swerving, or other aggressive/erratic maneuvers).   (ii) Bookmarking—Computer  312  may mark the occurrence of one or more of the above-described detected events in the metadata for the captured image data (or telemetry data) and/or separate data file which corresponds to the captured image data (or telemetry data). These bookmarks act as a timestamp on events of interest to facilitate a user to quickly access the captured images and other telemetry pertaining to an event of interest.   (iii) Activating emergency lights—The computer  312  may be configured with instructions to activate the emergency light bar and/or siren.   (iv) Intervening in driving dynamics—The computer  312  may be configured with instructions to intervene into the driver&#39;s own control of the vehicle, such as applying brake pressure to one or more wheels, overriding or limiting the driver&#39;s demand for throttle, reducing ignition or fuel, and/or limiting boost pressure in the case of a forced induction engine.   (v) Displaying an image—A video or still image which is representative of the event (e.g. the neglected stop or speed signage, the near collision, etc.) may be displayed on the vehicle display. A snapshot can be displayed, highlighting or putting a bounding box around the neglected stop or speed signage, the object which the vehicle nearly collided with, etc.   (vi) Activating data recording—An onboard storage device may start saving the information being captured by the vehicle&#39;s camera device. Other camera devices in the vehicle can be triggered to start recording. The captured information may also be transmitted concurrently via the communication network to be stored at the police station or another location.   (vii) Communicating information relating to the detected event to another location/user—The telemetry may be transmitted via the communication network to the police station or other locations (e.g., other police officers in the area). Information such as the driver&#39;s detected driving behavior, location (latitude/longitude), direction of travel, speed and time can be transmitted to police dispatch or other remote location. For example, associated metadata and/or a snapshot can be transmitted to a remote location or to any or all other computer devices or members/users in the group or to a dispatch/command center. For example, this would provide data to the remote individual (e.g. dispatch/command center) that the system had detected certain driving behavior and the individual could contact the officer to determine the reason for the officer&#39;s driving behavior.   (viii) Training the driver—When the above-referenced driving behavior is detected, the telemetry could be compiled with the synchronized audio and video associated with the event and presented to the driver in a user interface featuring corrective training. Additionally, the user interface could include a history of driving events for each officer.       

     With reference again to  FIG. 14 , it is noted that system  300  can detect not only events pertaining to vehicle  310  (such as described above), but also such events pertaining to other vehicles. System  300  may use the above-described components of system  300  (i.e. sensors, computer  312 , etc. of vehicle  310 ) to detect these events pertaining to other vehicles; the events pertaining to other vehicles may be detected by system  300  without requiring use of such components (sensors, etc.) disposed on the other vehicles. As a non-limiting example, system  300  can monitor various driving events of other drivers. For example, as illustrated in  FIG. 17 , depicts a system  300  can detect when another vehicle  350  (which will be referred to as a second vehicle) fails to stop at a traffic light  341  (or at stop sign  331 ,  FIG. 16 ). For example, but not by way of limitation, the analytics engine can recognize the traffic light  341  in image data captured by camera device  316 . As the second vehicle  350  approaches the traffic light  341 , the traffic light  341  becomes larger relative to the size of the video frame  320  of camera  316 . If the second vehicle  350  slows and stops at the traffic light  341 , the size of the second vehicle  350  should increase at the same proportion to the traffic light  341 . If the size of the second vehicle  350  does not, then that is indicative of the second vehicle  350  failing to stop at the traffic light  341 . As another non-limiting example, a video camera (e.g.,  316 ,  FIG. 14 ) in a stationary vehicle (e.g.,  310 ) may monitor an intersection, capturing video of the vehicles going through the intersection. The video camera may employ a virtual line, corresponding to a line marked on the roadway. The roadway line would demarcate the beginning of an intersection, crosswalk, etc. at a stop sign or traffic light. Using the virtual line and the image of the vehicle  350  in the display, the video camera may determine whether or not the vehicle  350  comes to a complete stop before passing the line, i.e., before passing the stop sign or traffic light. Using such techniques, system  300  can determine if the second vehicle  350  has failed to stop at a traffic light or a stop sign. 
     In another non-limiting example, the computer  312  of system  300  can also detect whether the second vehicle  350  is speeding. Using methods similar to those discussed, above, the analytics engine can determine the relative speed of the second vehicle  350  to the first vehicle  310  (i.e. the speed of the second vehicle  350  relative to the speed of the first vehicle  310 ). For example, the analytics engine can recognize the second vehicle  350  in the captured image data from video frame  320 . The image data can be sampled at a known rate. The computer  312  can calculate the change in size of the known objects relative to the entire video frame as discussed above. In other words, the rate at which the second vehicle  350  appears to increase (or decrease) can be used to calculate the relative speed of the vehicle  310  to the second vehicle  350 . Also, using the methods discussed, above, system  300  can sample the telemetry from the speed sensor  304 . Adding the speed of the first vehicle  310  to the relative speed of the second vehicle  350  would yield the speed of the second vehicle  350 . Computer  312  may also determine the speed of second vehicle  350 , and hence determine if vehicle  350  is speeding, in other ways. For example, video data of the second vehicle  350  could be captured continually for a period of time. As the video progresses frame by frame, the second vehicle  350  appearing in the video display moves across the display. The distance the second vehicle  350  moves across the display from an initially measured/recorded position in a given frame to a subsequently measured/recorded position in a given subsequent frame can be measured. These positions and this distance in the display can be correlated with actual physical positions and distance. In addition, the frame rate of the video (e.g., 30 frames per second) is known. Accordingly, it may be determined how far the second vehicle  350  moves in a given period of time. From this, the speed of second vehicle  350  can be determined. As described here, the computer  312  can thus in various ways determine the speed of second vehicle  350  without use of sensors (speed sensor, accelerometer, GPS device, etc.). Computer  312  may use multiple different methods to determine the speed of second vehicle  350 , for example, one or more methods may be used to corroborate results obtained another one or more methods. System  300  could store the speed of the second vehicle  350  as a separate file in internal memory  313  or store the telemetry data as metadata with the video data of the image data captured from the camera device  316 . This method could be useful to law enforcement agencies for detecting speeding without emitting electromagnetic radiation or light, which are detectable with radar or laser detectors. 
     According to another embodiment, a vehicle may be provided with a display (e.g., a dash cam) configured to display detected events. In some cases, the events displayed may be events of the vehicle itself, e.g., the display of vehicle  310  displays the fact that vehicle  310  is speeding. In some cases, the events displayed may be events of another vehicle, e.g., the display of vehicle  310  displays the fact that vehicle  350  is speeding. In some cases, the events displayed may be events of the vehicle itself and events of another vehicle. When an event of a given vehicle (e.g., the vehicle is speeding) is detected, the device (e.g., computer  312 ) detecting the event may send a notification of the detected event to a remote (e.g., cloud-based) server. Upon receipt of the notification, the server may send out a corresponding notification of the detected event to others (e.g., vehicles/drivers) to alert them of the detected event that occurred with the given vehicle. Notifications may be sent to others within a designated radius (distance) of the detected event or within a designated radius (distance) of the vehicle with respect to which the event was detected. This arrangement could be used to alert the police, other citizens/drivers, or even pedestrians nearby (e.g., by flashing the alert on a road sign). These notifications could serve, e.g., to alert the police and innocent citizens of a danger such as a reckless driver in the vicinity. Citizens would then be better able to protect themselves from such dangers (such as being hit by the reckless driver), by being on guard when driving or walking near, or crossing, a road in the vicinity of the vehicle in question. As for the content of the notification, it may include identification of the vehicle with respect to which the event was detected (e.g., the speeding vehicle), such as license plate information, vehicle make/model/color/year, etc., and identification of the nature of the event (e.g., reckless driving, speeding, going through a red light, etc.) and the location of the event (e.g., street address, distance from the recipient of the notification, etc.). The notification may also include still images or video clips of the vehicle (or another vehicle of the same make/model/color/year, etc.) and/or the detected event. This content may be displayed, as appropriate, as text (displayed and/or audible) and/or image/video, on the display of the vehicle receiving the notification. Citizens or drivers may register with a service or with the police in order to participate, that is, either to have their vehicles be monitored for events and/or to receive notifications of detected events of other vehicles. In some embodiments, self-driving vehicles would participate as monitored vehicles and/or notification recipients. In a network of self-driving vehicles, all the self-driving vehicles may be monitored and receive notifications (at least from other self-driving vehicles in the network). The travel vectors or trajectories of the self-driving vehicles may be known/obtained by a service, and the service may send notifications to recipient parties projected to be soon in the vicinity of the vehicle with respect to which an event was detected. Also, the notifications may include (in addition to content noted above) the travel vector/trajectory of the vehicle with respect to which an event was detected. 
       FIG. 19  is a flow chart illustrating a method according to one or more embodiments described in greater detail above. According to illustrated method  1900 , at block  1901  image data is captured using a camera device mounted to a vehicle. At block  1902 , telemetry data is sampled from at least one sensor mounted to the vehicle. At block  1903 , the occurrence of at least one designated event is detected from at least one of (i) the telemetry data and (ii) the image data. 
       FIG. 20  is a flow chart illustrating a method according to one or more embodiments described in greater detail above. According to illustrated method  2000 , at block  2001  image data is captured using a camera device mounted to a first vehicle. At block  2002 , telemetry data is sampled from at least one sensor mounted to the first vehicle. At block  2003 , an applicable speed limit is determined. At block  2004 , a first speed of the first vehicle is determined using at least one of (i) the image data captured by the camera mounted to the first vehicle, and (ii) the telemetry data sampled from the at least one sensor mounted on the first vehicle. At block  2005 , a relative speed of a second vehicle to the first vehicle is calculated using the image data captured by the camera device mounted to the first vehicle. At block  2006 , a second speed of the second vehicle is calculated by adding together (i) the relative speed of the second vehicle to the first vehicle and (ii) the first speed of the first vehicle. At block  2007 , the second speed of the second vehicle is compared with the applicable speed limit. 
     With regard to the various determinations that computer  312  or the analytics engine may make, it will be understood that in some cases more than one method may be used to determine a given piece of data (e.g., speed, acceleration, etc.) for purposes to of corroboration, verification, validation, triangulation (i.e., cross-checking multiple data sources and collection procedures to evaluate the extent to which all evidence converges), etc. 
     In light of the principles and example embodiments described and depicted herein, it will be recognized that the example embodiments can be modified in arrangement and detail without departing from such principles. Also, the foregoing discussion has focused on particular embodiments, but other configurations are also contemplated. In particular, even though expressions such as “in one embodiment,” “in another embodiment,” or the like are used herein, these phrases are meant to generally reference embodiment possibilities, and are not intended to limit the invention to particular embodiment configurations. As used herein, these terms may reference the same or different embodiments that are combinable into other embodiments. As a rule, any embodiment referenced herein is freely combinable with any one or more of the other embodiments referenced herein, and any number of features of different embodiments are combinable with one another, unless indicated otherwise. 
     Similarly, although example processes have been described with regard to particular operations performed in a particular sequence, numerous modifications could be applied to those processes to derive numerous alternative embodiments of the present invention. For example, alternative embodiments may include processes that use fewer than all of the disclosed operations, processes that use additional operations, and processes in which the individual operations disclosed herein are combined, subdivided, rearranged, or otherwise altered. This disclosure describes one or more embodiments wherein various operations are performed by certain systems, applications, modules, components, etc. In alternative embodiments, however, those operations could be performed by different components. Also, items such as applications, modules, components, etc., may be implemented as software constructs stored in a machine accessible storage medium, such as an optical disk, a hard disk drive, etc., and those constructs may take the form of applications, programs, subroutines, instructions, objects, methods, classes, or any other suitable form of control logic; such items may also be implemented as firmware or hardware, or as any combination of software, firmware and hardware, or any combination of any two of software, firmware and hardware. 
     This disclosure may include descriptions of various benefits and advantages that may be provided by various embodiments. One, some, all, or different benefits or advantages may be provided by different embodiments. 
     In view of the wide variety of useful permutations that may be readily derived from the example embodiments described herein, this detailed description is intended to be illustrative only, and should not be taken as limiting the scope of the invention. What is claimed as the invention, therefore, are all implementations that come within the scope of the following claims, and all equivalents to such implementations.