OBJECT-AWARE TEMPERATURE ANOMALIES MONITORING AND EARLY WARNING BY COMBINING VISUAL AND THERMAL SENSING SENSING

An apparatus including an interface and a processor. The interface may be configured to receive pixel data generated by a capture device and a temperature measurement generated by a thermal sensor. The processor may be configured to receive the pixel data and the temperature measurement from the interface, generate video frames in response to the pixel data, perform computer vision operations on the video frames to detect objects, perform a classification of the objects detected based on characteristics of the objects, detect a temperature anomaly in response to the temperature measurement and the classification, and generate a control signal in response to the temperature anomaly. The control signal may provide a warning based on the temperature anomaly. The classification may provide a normal temperature range for the objects detected.

This application relates to Chinese Application No.202010776855.9, filed August5,2020, which is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

The invention relates to computer vision generally and, more particularly, to a method and/or apparatus for implementing object-aware temperature anomalies monitoring and early warning by combining visual and thermal sensing.

BACKGROUND

Prevention of fires is an effective strategy for avoiding damage to people and property. Temperature anomalies are important early indicators of various disasters such as fires. Effectively detecting temperature anomalies can predict fires. Thermal sensing can be used to detect global anomalies in simple scenarios like forest fires. However, thermal sensing is a blunt tool for predicting fires. Thermal sensing tends to miss true alarms or triggers false alarms in complex scenarios such as parking lots, buildings and streets.

Context is an important consideration when detecting temperature anomalies. For example, a surface that is exposed to sunlight for long periods of time will become very hot, but might not pose a fire risk. Batteries of electric vehicles pose a thermal runaway risk that can result in a fire. Detecting a temperature anomaly in a battery could predict a potential fire. However, thermal sensors are not object-aware. A thermal sensor cannot distinguish between a hot surface (i.e., from sunlight) and a battery that is rapidly heating due to thermal runaway. Without context, the thermal sensor will miss scenarios that actually pose a risk of fire or falsely provide a warning when there is no fire risk.

It would be desirable to implement object-aware temperature anomalies monitoring and early warning by combining visual and thermal sensing.

SUMMARY

The invention concerns an apparatus comprising an interface and a processor. The interface may be configured to receive pixel data generated by a capture device and a temperature measurement generated by a thermal sensor. The processor may be configured to receive the pixel data and the temperature measurement from the interface, generate video frames in response to the pixel data, perform computer vision operations on the video frames to detect objects, perform a classification of the objects detected based on characteristics of the objects, detect a temperature anomaly in response to the temperature measurement and the classification, and generate a control signal in response to the temperature anomaly. The control signal may provide a warning based on the temperature anomaly. The classification may provide a normal temperature range for the objects detected.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Embodiments of the present invention include providing object-aware temperature anomalies monitoring and early warning by combining visual and thermal sensing that may (i) make a decision using sensor fusion, (ii) make inferences based on multiple sources of data, (iii) detect thermal runaway in a battery, (iv) monitor electric vehicles to predict a potential fire, (v) detect and classify objects, (vi) monitor specific features of objects based on a classification, (vii) prevent false alarms, (viii) implement computer vision efficiently using dedicated hardware modules and/or (ix) be implemented as one or more integrated circuits.

Embodiments of the present invention may implement an object-aware temperature anomaly monitoring system. The object-aware temperature anomaly warning system may be configured to provide an early warning for various hazards. For example, the early warning may be provided when a potential fire risk is detected.

Embodiments of the present invention may implement object-aware temperature anomaly monitoring by combining visual sensing and thermal sensing. Regions of temperature anomalies may be detected and/or localized in both thermal images and visual images. In an example, the visual images may comprise video frames (e.g., one or more RGB images). Computer vision may be applied to the combination of data generated by a visible light imaging sensor and a thermal imaging sensor. Computer vision operations may be applied to the thermal image or the visual image. The computer vision operations may be applied to both the thermal image and the visual image (e.g., sensor fusion).

The object-awareness may be provided by implementing artificial intelligence and/or computer vision technologies. Computer vision operations may be performed on video frames. The computer vision may be configured to detect objects and/or classify/identify the detected objects according to a category. Each category of object may comprise data about features and/or normal temperature ranges for the particular type of object.

The thermal sensor may be configured to measure temperatures of an area to detect regions that may be considered a temperature anomaly. In one example, a temperature anomaly may be a temperature that is outside (e.g., greater than) a normal operating temperature range. In another example, the temperature anomaly may be a temperature that quickly increases within a particular period of time (e.g., a rapid change in temperature). The temperature measurement alone may not be sufficient information to use as the basis for providing a warning. Data extracted from the video frames using the computer vision may be combined with the temperature measurements performed by the thermal sensor. Objects may be detected and classified in the regions identified as having temperature anomalies. A decision may be made about whether to trigger an early warning according to the normal temperature range of that object category. For example, if the temperature anomaly is outside (e.g., above) the normal temperature range for the class of object detected, then an early warning may be generated. Data combined from the RGB image and the thermal image may be used to determine where the temperature anomaly is happening (e.g., identify a feature of the detected object that corresponds to the temperature anomaly).

Embodiments of the present invention may be configured to perform early detection and/or provide relevant warnings for electrical vehicle (EV) fires. In an example, the computer vision operations may be configured to classify/identify an EV based on license plate recognition and/or car model recognition using the RBG image first (e.g., performing computer vision on video frames), while the thermal camera may detect regions of temperature anomalies. A sudden and/or rapid temperature increase may be one example of a temperature anomaly. The object-awareness may enable the location and/or feature of the EV that is causing the temperature anomaly to be identified. In an example, if the bottom of an EV corresponds to the location of the detected temperature anomaly, the classification of the EV may provide information that the battery is located on the bottom of the EV. The classification and/or the location of the temperature anomaly may be used to determine that the battery may be the feature of the EV that is the source of the temperature anomaly. An early warning of a battery fire may be generated. The decision to generate the early warning may be based on the joint sources of data (e.g., based on object detection and recognition using RGB image, and rapid temperature change using thermal image).

Referring toFIG. 1, a diagram illustrating an example context of an embodiment of the present invention is shown. An example scenario50is shown as context. The example scenario50may comprise surfaces52a-52c. In one example, the surface52amay be a wall. The surface52bmay be the ground. The surface52cmay be a ceiling. In an example, the surfaces52a-52cmay be a parking garage. Vehicles60a-60nare shown on the ground52b. In an example, the vehicles60a-60nmay be parked and left unattended (e.g., not monitored by a person). In another example, the surface52amay be a charging station (e.g., for charging an electric vehicle).

A block (or circuit)100is shown. The circuit may implement a camera system. In the example scenario50, the camera system100may be mounted to the ceiling52c. In some embodiments, the camera system100may be implemented in an outdoor environment (e.g., with no ceiling52c). The camera system100may alternatively be mounted to the wall52band/or another vertical structure (e.g., a lamp post, a billboard, a sign, etc.). In the example shown, the camera system100is shown mounted from a relatively high location. In some embodiments, the camera system100may be mounted at a lower position. In an example, the surface52bmay be a charging station and the camera system100may be mounted to the charging station52bto enable the camera system100to monitor electric vehicles connected to a charging station. The mounting location of the camera system100may be varied according to the design criteria of a particular implementation.

A block (or circuit)102and/or a block (or circuit)104is shown. The circuit102may implement a capture device. The circuit104may implement a thermal sensor. In the example shown, the capture device102and the thermal sensor104may be external to the camera system100. In some embodiments, one or both of the capture device102and the thermal sensor104may be internal components of the camera system100. For example, the capture device102may be implemented internal to the camera system100(e.g., a lens may be implemented on the camera system100to enable light to be captured by the capture device102). In the example shown, the one capture device102and one thermal sensor104may be connected to the camera system100. In some embodiments, multiple capture devices102a-102n(not shown) and/or multiple thermal sensors104a-104n(not shown) may be connected to the camera system100. The arrangement of the camera system100, the capture device102and/or the thermal sensor104may be varied according to the design criteria of a particular implementation. Details of the components and/or signals generated by the camera system100, the capture device102and/or the thermal sensor104may be explained in more detail in association withFIG. 2.

Dotted lines110a-110bare shown extending from the capture device102. The dotted lines110a-110bmay represent a region of interest (e.g., field of view) of the capture device102. In an example, the capture device102may implement a RGB camera and the capture device102may generate pixel data of an area within the range of the region of interest110a-110b. The camera system100may be configured to generate video frames in response to the pixel data generated by the capture device102. In some embodiments, the region of interest110a-110bmay comprise a wide-angle field of view. The camera system100may be configured to perform de-warping operations to correct distortions caused by the wide angle lenses used to capture the wide-angle field of view110a-110b. The vehicles60a-60nare shown within the region of interest110a-110b. In an example, the capture device102may be configured to provide video monitoring of the vehicles60a-60n(e.g., a parking garage camera).

Dotted lines112a-112bare shown extending from the thermal sensor104. The dotted lines112a-112bmay represent a monitoring range (e.g., thermal region of interest) of the thermal sensor104. The thermal sensor104may be configured to perform temperature measurements within the monitoring range112a-112b. The vehicles60a-60nare shown within the monitoring range112a-112bof the thermal sensor104.

The thermal sensor104may be configured to measure temperatures at a distance. For example, the thermal sensor104may not need to be close up to the vehicles60a-60nto perform temperature measurements of the vehicles60a-60n. A shaded region120is shown. The shaded region120may represent the temperature measurement at a distance. In the example shown, the shaded region may perform a distance temperature measurement of the vehicle60b.

An area122is shown at the end of the distance temperature measurement120. The area122may represent a surface temperature measurement. In the example shown, the thermal sensor104may perform a temperature measurement of the surface (e.g., the hood) of the vehicle60b. While one area122is shown in the example scenario50, the thermal sensor104may be configured to measure the surface temperature within the entire monitoring range112a-112b. For example, the thermal sensor104may be configured to generate thermal images comprising temperature measurements of all surfaces within the monitoring range112a-112b(e.g., a heat map).

In the example shown, the region of interest110a-110bis shown covering an area different from the monitoring region112a-112bof the thermal sensor104. The differences between the location of the region of interest110a-110band the monitoring region112a-112bmay be shown for illustrative purposes. In some embodiments, the region of interest110a-110bmay cover the same area as the monitoring region112a-112b. For example, by covering the same areas, the thermal sensor104may provide temperature measurements that correspond to locations in the video frames generated in response to pixel data generated by the capture device102.

In some embodiments, capture device102and the thermal sensor104may provide mounting location and/or view angle information to the camera system100. The mounting location and/or view angle information may enable the camera system100to determine a disparity between the region of interest110a-110band the monitoring region112a-112b. Determining the disparity between the region of interest110a-110band the monitoring region112a-112bmay enable the camera system100to correlate the temperature measurements performed by the thermal sensor104at a particular time, to a location in the video frames generated from pixel data captured by the capture device102at a particular time.

Referring toFIG. 2, a block diagram illustrating an example embodiment of the invention is shown. The apparatus100is shown. The apparatus100generally comprises the capture devices102a-102n, the thermal sensor104, blocks (or circuits)150a-150n, a block (or circuit)154, a block (or circuit)156, a block (or circuit)158, blocks (or circuits)160a-160nand/or blocks (or circuits)162a-162n. The capture devices102a-102nmay be one or more implementations of the capture device102shown in association withFIG. 1. The blocks150a-150nmay implement lenses. The circuit154may implement a communication device. The circuit156may implement a processor. The circuit158may implement a memory. The circuits160a-160nmay implement microphones (e.g., audio capture devices). The circuits162a-162nmay implement audio output devices (e.g., speakers). The apparatus100may comprise other components (not shown). In the example shown, some of the thermal sensor104, the lenses150a-150n, the communication device154and the speakers162a-162nare shown external to the camera system100. However, the various components of the camera system100may be implemented within and/or attached to the camera system100(e.g., the speakers162a-162nmay provide better functionality if not located inside a housing of the camera system100). The number, type and/or arrangement of the components of the apparatus100may be varied according to the design criteria of a particular implementation.

In an example implementation, the circuit156may be implemented as a video processor. The processor156may comprise inputs170a-170nand/or other inputs. The processor156may comprise an input/output172. The processor156may comprise an output174aand an input174b. The processor156may comprise an input176. The processor156may comprise an output178and/or other outputs. The processor156may comprise an input180. The number of inputs, outputs and/or bi-directional ports implemented by the processor156may be varied according to the design criteria of a particular implementation.

In the embodiment shown, the capture devices102a-102nmay be components of the apparatus100. In some embodiments, the capture devices102a-102nmay be separate devices (e.g., remotely connected to the camera system100, such as a drone, a robot and/or a system of security cameras configured capture video data) configured to send data to the apparatus100. In one example, the capture devices102a-102nmay be implemented as part of an autonomous robot configured to patrol particular paths such as hallways and/or parking garages. Similarly, in the example shown, the wireless communication device154, the microphones160a-160nand/or the speakers162a-162nare shown external to the apparatus100but in some embodiments may be a component of (e.g., within) the apparatus100.

The apparatus100may receive one or more signals (e.g., IMF_A-IMF_N), a signal (e.g., FEAT_SET), a signal (e.g., THIMG) and/or one or more signals (e.g., DIR_AUD). The apparatus100may present a signal (e.g., ANOM), a signal and/or a signal (e.g., DIR_AOUT). The capture devices102a-102nmay receive the signals IMF_A-IMF_N from the corresponding lenses150a-150n. The processor156may receive the signal DIR_AUD from the microphones160a-160n. The processor156may present the signal ANOM to the communication device154and receive the signal FEAT_SET from the communication device154. For example, the wireless communication device154may be a radio-frequency (RF) transmitter. In another example, the communication device154may be a Wi-Fi module. In another example, the communication device154may be a device capable of implementing RF transmission, Wi-Fi, Bluetooth and/or other wireless communication protocols. The processor156may present the signal DIR_AOUT to the speakers162a-162n.

The lenses150a-150nmay capture signals (e.g., IM_A-IM_N). The signals IM_A-IM_N may be an image (e.g., an analog image) of the environment near the camera system100presented by the lenses150a-150nto the capture devices102a-102nas the signals IMF_A-IMF_N. The environment near the camera system100may be the field of view110a-110n. The lenses150a-150nmay be implemented as an optical lens. The lenses150a-150nmay provide a zooming feature and/or a focusing feature. The capture devices102a-102nand/or the lenses150a-150nmay be implemented, in one example, as a single lens assembly. In another example, the lenses150a-150nmay be a separate implementation from the capture devices102a-102n. The capture devices102a-102nare shown within the circuit100. In an example implementation, the capture devices102a-102nmay be implemented outside of the circuit100(e.g., along with the lenses150a-150nas part of a lens/capture device assembly).

The capture devices102a-102nmay be configured to capture image data for video (e.g., the signals IMF_A-IMF_N from the lenses150a-150n). In some embodiments, the capture devices102a-102nmay be video capturing devices such as cameras. The capture devices102a-102nmay capture data received through the lenses150a-150nto generate raw pixel data. In some embodiments, the capture devices102a-102nmay capture data received through the lenses150a-150nto generate bitstreams (e.g., generate video frames). For example, the capture devices102a-102nmay receive focused light from the lenses150a-150n. The lenses150a-150nmay be directed, tilted, panned, zoomed and/or rotated to provide a targeted view from the camera system100(e.g., to provide coverage for a panoramic field of view such as the field of view110a-110b). The capture devices102a-102nmay generate signals (e.g., PIXELD_A-PIXELD_N). The signals PIXELD_A-PIXELD_N may be pixel data (e.g., a sequence of pixels that may be used to generate video frames). In some embodiments, the signals PIXELD_A-PIXELD_N may be video data (e.g., a sequence of video frames). The signals PIXELD_A-PIXELD_N may be presented to the inputs170a-170nof the processor156.

The capture devices102a-102nmay transform the received focused light signals IMF_A-IMF_N into digital data (e.g., bitstreams). In some embodiments, the capture devices102a-102nmay perform an analog to digital conversion. For example, the capture devices102a-102nmay perform a photoelectric conversion of the focused light received by the lenses150a-150n. The capture devices102a-102nmay transform the bitstreams into pixel data, images and/or video frames. In some embodiments, the pixel data generated by the capture devices102a-102nmay be uncompressed and/or raw data generated in response to the focused light from the lenses150a-150n. In some embodiments, the output of the capture devices102a-102nmay be digital video signals.

The thermal sensor104may be configured to generate temperature measurements. The temperature measurements may comprise a thermal image. The thermal sensor104may generate a thermal image of the temperature monitoring region112a-112b. The thermal image generated may be a low resolution of data (e.g., temperature measurement data).

The thermal sensor104may receive a signal (e.g., TEMP). The signal TEMP may be the temperature detected by the thermal sensor within the temperature monitoring region112a-112b. The thermal sensor104may generate the signal THIMG. The signal THIMG may be generated in response to the signal TEMP detected. In one example, the signal THIMG may be the thermal image. In another example, the signal THIMG may be a data set of temperatures of the thermal monitoring region112a-112bcaptured at a particular time and the processor156may generate the thermal image from the data set of temperature measurements. The signal THIMG may be presented to the input180of the processor156.

The communication device154may send and/or receive data to/from the apparatus100. In some embodiments, the communication device154may be implemented as a wireless communications module. In some embodiments, the communication device154may be implemented as a satellite connection to a proprietary system. In one example, the communication device154may be a hard-wired data port (e.g., a USB port, a mini-USB port, a USB-C connector, HDMI port, an Ethernet port, a DisplayPort interface, a Lightning port, etc.). In another example, the communication device154may be a wireless data interface (e.g., Wi-Fi, Bluetooth, ZigBee, cellular, etc.).

The communication device154may be configured to receive the signal FEAT_SET. The signal FEAT_SET may comprise a feature set that corresponds to the classification of various objects (e.g., people, animals, vehicles, street signs, etc.). The feature set information may comprise instructions for the processor156to classify various objects and/or localize objects within a video frame.

The processor156may receive the signals PIXELD_A-PIXELD_N from the capture devices102a-102nat the inputs170a-170n. The processor156may send/receive a signal (e.g., DATA) to/from the memory158at the input/output172. The processor156may send the signal ANOM to the communication device154via the output port174a. The processor156may receive the signal FEAT_SET from the communication device154via the input port174b. The processor156may receive the signal DIR_AUD from the microphones160a-160nat the port176. The processor156may send the signal DIR_AOUT to the speakers162a-162nvia the port178. The processor156may receive the signal THIMG at the interface port180. In an example, the processor156may be connected through a bi-directional interface (or connection) to the capture devices102a-102n, the thermal sensor104, the communication device154, the memory158, the microphones160a-160nand/or the speakers162a-162n. The processor156may store and/or retrieve data from the memory158. The memory158may be configured to store computer readable/executable instructions (or firmware). The instructions, when executed by the processor156, may perform a number of steps.

The signal PIXELD_A-PIXELD_N may comprise raw pixel data providing a field of view captured by the lenses150a-150n. The processor156may be configured to generate video frames from the pixel data PIXELD_A-PIXELD_N. The video frames generated by the processor156may be used internal to the processor156. In some embodiments, the video frames may be communicated to the memory158for temporary storage.

The processor156may be configured to make decisions based on analysis of the video frames generated from the signals PIXELD_A-PIXELD_N. The processor156may generate the signal ANOM, the signal DATA, the signal DIR_AOUT and/or other signals (not shown). The signal ANOM, the signal DATA and/or the signal DIR_AOUT may each be generated (in part) based on one or more decisions made and/or functions performed by the processor156. The decisions made and/or functions performed by the processor156may be determined based on data received by the processor156at the inputs170a-170n(e.g., the signals PIXELD_A-PIXELD_N), the input172, the input174b, the input176, the input180and/or other inputs.

The inputs170a-170n, the input/output172, the output174a, the input174b, the input176, the output178, the input180and/or other inputs/outputs may implement an interface. The interface may be implemented to transfer data to/from the processor156, the capture devices102a-102n, the thermal sensor104, the communication device154, the memory158, the microphones160a-160n, the speakers162a-162nand/or other components of the apparatus100. In one example, the interface may be configured to receive (e.g., via the inputs170a-170n) the pixel data signals PIXELD_A-PIXELD_N each from a respective one of the capture devices102a-102n. In another example, the interface may be configured to receive (e.g., via the input176) the directional audio DIR_AUD. In yet another example, the interface may be configured to transmit information about a temperature anomaly (e.g., the signal ANOM) and/or the converted data determined based on the computer vision operations to the communication device154. In still another example, the interface may be configured to receive the feature set information FEAT_SET (e.g., via the input port174b) from the communication device154. In another example, the interface may be configured to transmit directional audio output (e.g., the signal DIR_AOUT) to each of the speakers162a-162n. In yet another example, the interface may be configured to to receive (e.g., via the input180) the thermal image data THIMG from the thermal sensor104. The interface may be configured to enable transfer of data and/or translate data from one format to another format to ensure that the data transferred is readable by the intended destination component. In an example, the interface may comprise a data bus, traces, connectors, wires and/or pins. The implementation of the interface may be varied according to the design criteria of a particular implementation.

The signal ANOM may be presented to the communication device154. In some embodiments, the signal ANOM may comprise parameters and/or statistics determined by the processor156about the video frames. The signal ANOM may be generated in response to the computer vision operations and/or sensor fusion operations performed. The signal ANOM may comprise information about a detected temperature anomaly. In some embodiments, the signal ANOM may comprise video frames that correspond to the detected temperature anomaly. The video frames may be encoded, cropped, stitched and/or enhanced versions of the pixel data received from the signals PIXELD_A-PIXELD_N. In an example, the video frames may be a high resolution, digital, encoded, de-warped, stabilized, cropped, blended, stitched and/or rolling shutter effect corrected version of the signals PIXELD_A-PIXELD_N. The signal ANOM may comprise data about the temperature anomaly such as temperatures detected, the classification of the object associated with the temperature anomaly, the rate of temperature change detected, the type of potential hazard detected (e.g., potential fire detected), the location of the temperature anomaly, etc.

The communication device154may be configured to generate a signal (e.g., ALERT). The signal ALERT may be an early warning corresponding to the temperature anomaly detected. For example, the communication device154may generate the signal ALERT in response to the signal ANOM. The signal ALERT may be a packetized version of the signal ANOM that may be communicated according to a particular communications protocol (e.g., Wi-Fi, SMS, Bluetooth, etc.).

In some embodiments, the signal ALERT may be a text message (e.g., a string of human readable characters). In some embodiments, the signal ANOM may be a symbol that indicates an event or status (e.g., a fire symbol, a high heat symbol, a battery explosion symbol, etc.). The signal ANOM and/or the signal ALERT may be generated based on video analytics (e.g., computer vision operations) performed by the processor156on the video frames generated from the pixel data PIXELD_A-PIXELD_N and/or the data from the thermal image THIMG. The processor156may be configured to perform the computer vision operations to detect objects and/or events in the video frames and then convert the detected objects and/or events into statistics and/or parameters.

The computer vision operations performed by the processor156may comprise object detection (e.g., bounding boxes that locate positions of objects), classification (inferring that the detected object is a particular type of object), segmentation (e.g., dividing the image into various regions based on characteristics of pixels to identify objects/boundaries), etc. The data determined by the computer vision operations may be converted to the human-readable format by the processor156. The data from the computer vision operations that has been converted to the human-readable format may be communicated as the signal ALERT. The signal ALERT may be communicated to a particular person, a monitoring service and/or an emergency response service (e.g., fire department).

The apparatus100may implement a camera system. In some embodiments, the camera system100may be implemented as a drop-in solution (e.g., installed as one component). In an example, the camera system100may be a device that may be installed as an after-market product (e.g., a retro-fit for a drone, a retro-fit for a security system, etc.). In some embodiments, the apparatus100may be a component of a security system. The number and/or types of signals and/or components implemented by the camera system100may be varied according to the design criteria of a particular implementation.

The video data of the targeted view captured in the field of view110a-110bmay be generated from the signals/bitstreams/data PIXELD_A-PIXELD_N. The capture devices102a-102nmay present the signals PIXELD_A-PIXELD_N to the inputs170a-170nof the processor156. The signals PIXELD_A-PIXELD_N may be used by the processor156to generate the video frames/video data. In some embodiments, the signals PIXELD_A-PIXELD_N may be video streams captured by the capture devices102a-102n. In some embodiments, the capture devices102a-102nmay be implemented in the camera system100. In some embodiments, the capture devices102a-102nmay be configured to add to existing functionality to the camera system100.

Each of the capture devices102a-102nmay comprise a block (or circuit)182, a block (or circuit)184, and/or a block (or circuit)186. The circuit182may implement a camera sensor (e.g., a complementary metal-oxide-semiconductor (CMOS) sensor). The circuit184may implement a camera processor/logic. The circuit186may implement a memory buffer. As a representative example, the capture device102ais shown comprising the sensor182a, the logic block184aand the buffer186a. Similarly, the capture devices102b-102nmay comprise the camera sensors182b-182n, the logic blocks184b-184nand the buffers186b-186n. The sensors182a-182nmay each be configured to receive light from the corresponding one of the lenses150a-150nand transform the light into digital data (e.g., the bitstreams).

In one example, the sensor182aof the capture device102amay receive light from the lens150a. The camera sensor182aof the capture device102amay perform a photoelectric conversion of the light from the lens150a. In some embodiments, the sensor182amay be an oversampled binary image sensor. The logic184amay transform the bitstream into a human-legible content (e.g., pixel data and/or video data). For example, the logic184amay receive pure (e.g., raw) data from the camera sensor182aand generate pixel data based on the raw data (e.g., the bitstream). The memory buffer186amay store the raw data and/or the processed bitstream. For example, the frame memory and/or buffer186amay store (e.g., provide temporary storage and/or cache) the pixel data and/or one or more of the video frames (e.g., the video signal).

The microphones160a-160nmay be configured to capture incoming audio and/or provide directional information about the incoming audio. Each of the microphones160a-160nmay receive a respective signal (e.g., AIN_A-AIN_N). The signals AIN_A-AIN_N may be audio signals from the environment near the apparatus100. For example, the signals AIN_A-AIN_N may be ambient noise in the environment. The microphones160a-160nmay be configured to generate the signal DIR_AUD in response to the signals AIN_A-AIN_N. The signal DIR_AUD may be a signal that comprises the audio data from the signals AIN_A-AIN_N. The signal DIR_AUD may be a signal generated in a format that provides directional information about the signals AIN_A-AIN_N.

The microphones160a-160nmay provide the signal DIR_AUD to the interface176. The apparatus100may comprise the interface176configured to receive data (e.g., the signal DIR_AUD) from one or more of the microphones160a-160n. In one example, data from the signal DIR_AUD presented to the interface176may be used by the processor156to be used for sensor fusion analysis (e.g., a hissing sound may indicate a potential combustion).

The number of microphones160a-160nmay be varied according to the design criteria of a particular implementation. The number of microphones160a-160nmay be selected to provide sufficient directional information about the incoming audio (e.g., the number of microphones160a-160nimplemented may be varied based on the accuracy and/or resolution of directional information acquired). In an example, 2 to 6 of the microphones160a-160nmay be implemented. In some embodiments, an audio processing component may be implemented with the microphones160a-160nto process and/or encode the incoming audio signals AIN_A-AIN_N. In some embodiments, the processor156may be configured with on-chip audio processing to encode the incoming audio signals AIN_A-AIN_N. The microphones160a-160nmay capture audio of the environment. The apparatus100may be configured to synchronize the audio captured with the images captured by the capture devices102a-102n.

The processor156may be configured to execute computer readable code and/or process information. The processor156may be configured to receive input and/or present output to the memory158. The processor156may be configured to present and/or receive other signals (not shown). The number and/or types of inputs and/or outputs of the processor156may be varied according to the design criteria of a particular implementation.

The processor156may receive the signals PIXELD_A-PIXELD_N, the signal DIR_AUDIO, the signal FEAT_SET, the signal THIMG and/or the signal DATA. The processor156may make a decision based on data received at the inputs170a-170n, the input172, the input174b, the input176, the input180and/or other input. For example, other inputs may comprise external signals generated in response to user input, external signals generated by the microphones160a-160nand/or internally generated signals such as signals generated by the processor156in response to analysis of the video frames and/or objects detected in the video frames. The processor156may adjust the video data (e.g., crop, digitally move, physically move the camera sensors182a-182n, etc.) of the video frames. The processor156may generate the signal ANOM and/or the signal DIR_AOUT in response to data received by the inputs170a-170n, the input172, the input174b, the input176, the input180and/or the decisions made in response to the data received by the inputs170a-170n, the input172, the input174b, the input176and/or the input180.

The signal ANOM and/or the signal DIR_AOUT may be generated to provide an output in response to the captured video frames and the video analytics and/or the sensor fusion performed by the processor156. For example, the video analytics may be performed by the processor156in real-time and/or near real-time (e.g., with minimal delay).

The cropping, downscaling, blending, stabilization, packetization, encoding, compression and/or conversion performed by the processor156may be varied according to the design criteria of a particular implementation. For example, the video frames generated by the processor156may be a processed version of the signals PIXELD_A-PIXELD_N configured to enable detection of objects, classification of objects and/or determination of characteristics of the detected objects. In some embodiments, the video data may be encoded at a high bitrate. For example, the signal may be generated using a lossless compression and/or with a low amount of lossiness.

In some embodiments, the video frames may be some view (or derivative of some view) captured by the capture devices102a-102n. For example, the video frames may comprise a portion of the panoramic video captured by the capture devices102a-102n. In another example, the video frames may comprise a region of interest selected and/or cropped from the panoramic video frame by the processor156(e.g., upscaled, oversampled and/or digitally zoomed) to enable a high precision of object detection. In some embodiments, the video frames may provide a series of cropped and/or enhanced panoramic video frames that improve upon the view from the perspective of the camera system100(e.g., provides night vision, provides High Dynamic Range (HDR) imaging, provides more viewing area, highlights detected objects, provides additional data such as a numerical distance to detected objects, etc.) to enable the processor156to see the location50better than a person would be capable of with human vision.

The memory158may store data. The memory158may be implemented as a cache, flash memory, DRAM memory, etc. The type and/or size of the memory158may be varied according to the design criteria of a particular implementation. The data stored in the memory158may correspond to a video file, user profiles, user permissions, object classifications, normal operating temperatures and/or locations of various features of particular vehicles, etc.

The lenses150a-150n(e.g., camera lenses) may be directed to provide a panoramic view from the camera system100. The lenses150a-150nmay be aimed to capture environmental data (e.g., light). The lens150a-150nmay be configured to capture and/or focus the light for the capture devices102a-102n. Generally, the camera sensors182a-182nmay be located behind each of the respective lenses150a-150n. Based on the captured light from the lenses150a-150n, the capture devices102a-102nmay generate a bitstream and/or raw pixel data.

Embodiments of the processor156may perform video stitching operations on the signals PIXELD_A-PIXELD_N. In one example, each of the pixel data signals PIXELD_A-PIXELD_N may provide a portion of a panoramic view and the processor156may crop, blend, synchronize and/or align the pixel data from the signals PIXELD_A-PIXELD_N to generate the panoramic video frames. In some embodiments, the processor156may be configured to perform electronic image stabilization (EIS). The processor156may perform de-warping on the video frames. The processor156may perform intelligent video analytics on the de-warped video frames. The processor156discard the video frames after the video analytics and/or computer vision has been performed.

The processor156may receive an input to generate the video frames (e.g., the signals PIXELD_A-PIXELD_N) from the CMOS sensor(s)182a-182n. The pixel data signals PIXELD_A-PIXELD_N may be enhanced by the processor156(e.g., color conversion, noise filtering, auto exposure, auto white balance, auto focus, etc.). Generally, the panoramic video may comprise a large field of view generated by one or more lenses/camera sensors. One example of a panoramic video may be an equirectangular 360 video. Equirectangular 360 video may also be called spherical panoramas. Panoramic video may be a video that provides a field of view that is larger than the field of view that may be displayed on a device used to playback the video. For example, the field of view110a-110bcaptured by the camera system100may be used to generate panoramic video such as a spherical video, a hemispherical video, a 360 degree video, a wide angle video, a video having less than a 360 field of view, etc.

Panoramic videos may comprise a view of the environment near the camera system100. In one example, the entire field of view110a-110bof the panoramic video may be captured at generally the same time (e.g., each portion of the panoramic video represents the view from the camera system100at one particular moment in time). In some embodiments (e.g., when the camera system100implements a rolling shutter sensor), a small amount of time difference may be present between some portions of the panoramic video. Generally, each video frame of the panoramic video comprises one exposure of the sensor (or the multiple sensors182a-182n) capturing the environment near the camera system100.

In some embodiments, the field of view110a-110bmay provide coverage for a full 360 degree field of view. In some embodiments, less than a 360 degree view may be captured by the camera system100(e.g., a 270 degree field of view, a 180 degree field of view, etc.). In some embodiments, the panoramic video may comprise a spherical field of view (e.g., capture video above and below the camera system100). For example, the camera system100may be mounted on a ceiling and capture a spherical field of view of the area below the camera system100. In some embodiments, the panoramic video may comprise a field of view that is less than a spherical field of view (e.g., the camera system100may be configured to capture the ground below and the areas to the sides of the camera system100but nothing directly above). The implementation of the camera system100and/or the captured field of view110a-110bmay be varied according to the design criteria of a particular implementation.

In embodiments implementing multiple lenses, each of the lenses150a-150nmay be directed towards one particular direction to provide coverage for a full 360 degree field of view. In embodiments implementing a single wide angle lens (e.g., the lens150a), the lens150amay be located to provide coverage for the full 360 degree field of view (e.g., on the bottom of the camera system100in a ceiling mounted embodiment, on the bottom of a drone camera, etc.). In some embodiments, less than a 360 degree view may be captured by the lenses150a-150n(e.g., a 270 degree field of view, a 180 degree field of view, etc.). In some embodiments, the lenses150a-150nmay move (e.g., the direction of the capture devices may be controllable). In some embodiments, one or more of the lenses150a-150nmay be configured to implement an optical zoom (e.g., the lenses150a-150nmay zoom in/out independent of each other).

In some embodiments, the apparatus100may be implemented as a system on chip (SoC). For example, the apparatus100may be implemented as a printed circuit board comprising one or more components (e.g., the capture devices102a-102n, the processor156, the communication device154, the memory158, etc.). The apparatus100may be configured to perform intelligent video analysis on the video frames of the de-warped, panoramic video. The apparatus100may be configured to crop and/or enhance the panoramic video.

In some embodiments, the processor156may be configured to perform sensor fusion operations. The sensor fusion operations performed by the processor156may be configured to analyze information from multiple sources (e.g., the capture devices102a-102n, the thermal sensor104and the microphones160a-160n). By analyzing various data from disparate sources, the sensor fusion operations may be capable of making inferences about the data that may not be possible from one of the disparate data sources alone. For example, the sensor fusion operations implemented by the processor156may analyze video data (e.g., classify objects) as well as the temperature measurements from the thermal image THIMG. The disparate sources may be used to develop a model of a scenario to support decision making. The sensor fusion operations may also provide time correlation, spatial correlation and/or reliability among the data being received.

In some embodiments, the processor156may implement convolutional neural network capabilities. The convolutional neural network capabilities may implement computer vision using deep learning techniques. The convolutional neural network capabilities may be configured to implement pattern and/or image recognition using a training process through multiple layers of feature-detection.

The signal DIR_AOUT may be an audio output. For example, the processor156may generate output audio based on information extracted from the video frames PIXELD_A-PIXELD_N. The signal DIR_AOUT may be determined based on an event and/or objects determined using the computer vision operations. In one example, the signal DIR_AOUT may comprise an audio message providing information about a temperature anomaly. In some embodiments, the signal DIR_AOUT may not be generated until an event has been detected by the processor156using the computer vision operations.

The signal DIR_AOUT may comprise directional and/or positional audio output information for the speakers162a-162n. The speakers162a-162nmay receive the signal DIR_AOUT, process the directional and/or positional information and determine which speakers and/or which channels will play back particular audio portions of the signal DIR_AOUT. The speakers162a-162nmay generate the signals AOUT_A-AOUT_N in response to the signal DIR_AOUT. The signals AOUT_A-AOUT_N may be an audio message. In some embodiments, the audio message played by the speakers162a-162nmay be the early detecting warning of the potential hazard generated in response to the temperature anomaly. For example, the speakers162a-162nmay emit a pre-recorded message in response to a detected event. The signal DIR_AOUT may be a signal generated in a format that provides directional information for the signals AOUT_A-AOUT_N. The number of speakers162a-162nmay be varied according to the design criteria of a particular implementation. The number of speakers162a-162nmay be selected to provide sufficient directional channels for the outgoing audio (e.g., the number of speakers162a-162nimplemented may be varied based on the accuracy and/or resolution of directional audio output). In an example, 1 to 6 of the speakers162a-162nmay be implemented. In some embodiments, an audio processing component may be implemented by the speakers162a-162nto process and/or decode the output audio signals DIR_AOUT. In some embodiments, the processor156may be configured with on-chip audio processing.

The sensors182a-182nmay each implement a high-resolution sensor. Using the high resolution sensors182a-182n, the processor156may combine over-sampling of the image sensors182a-182nwith digital zooming within a cropped area. The over-sampling and digital zooming may each be one of the video operations performed by the processor156. The over-sampling and digital zooming may be implemented to deliver higher resolution images within the total size constraints of a cropped area.

In some embodiments, one or more of the lenses150a-150nmay implement a fisheye lens. One of the video operations implemented by the processor156may be a dewarping operation. The processor156may be configured to dewarp the video frames generated. The dewarping may be configured to reduce and/or remove acute distortion caused by the fisheye lens and/or other lens characteristics. For example, the dewarping may reduce and/or eliminate a bulging effect to provide a rectilinear image.

The processor156may be configured to crop (e.g., trim to) a region of interest from a full video frame (e.g., generate the region of interest video frames). The processor156may generate the video frames and select an area. In an example, cropping the region of interest may generate a second image. The cropped image (e.g., the region of interest video frame) may be smaller than the original video frame (e.g., the cropped image may be a portion of the captured video).

The area of interest may be dynamically adjusted based on the temperature measurements performed by the thermal sensor104. For example, a high temperature may be detected at a particular location in the thermal image signal THIMG. The processor156may update the selected region of interest coordinates and dynamically update the cropped section based on the locations of high temperatures detected by the thermal sensor104. The cropped section may correspond to the area of interest selected. As the area of interest changes, the cropped portion may change. For example, the selected coordinates for the area of interest may change from frame to frame, and the processor156may be configured to crop the selected region in each frame.

The processor156may be configured to perform a disparity correction between the thermal image and the video frames. In an example, there may be a disparity between the thermal image captured by the thermal sensor104and the video frames generated from the pixel data generated by the capture devices102a-102nresulting from the different mounting locations of the thermal sensor104and the capture devices102a-102n. In one example, the thermal image signal THIMG may comprise mounting location information (e.g., position, angle, tilt, zoom, etc.) of the thermal image sensor104at the time the thermal image was captured. Similarly, the signals PIXELD_A-PIXELD_N may comprise mounting location information (e.g., position, angle, tilt, zoom, etc.) of the capture devices102a-102nat the time the pixel data was captured. In another example, the memory158may store pre-determined mounting location information about the thermal image sensor104and/or the capture devices102a-102n(e.g., when the sensors are stationary). The processor156may be configured to use the mounting information to calculate the disparity between the fields of view of the thermal sensor104and the capture devices102a-102n. The disparity calculated may be used to enable an accurate and aligned comparison of the information from the thermal image to the information in the video frames. The disparity calculation may be used to correlate the temperature measured at a particular location in the thermal image to the same location in the visual image. The disparity calculation may ensure that the regions of the temperature anomalies are accurately mapped to the visual image.

The processor156may be configured to over-sample the image sensors182a-182n. The over-sampling of the image sensors182a-182nmay result in a higher resolution image. The processor156may be configured to digitally zoom into an area of a video frame. For example, the processor156may digitally zoom into the cropped area of interest. For example, the processor156may establish the area of interest based on the directional audio, crop the area of interest, and then digitally zoom into the cropped region of interest video frame.

The dewarping operations performed by the processor156may adjust the visual content of the video data. The adjustments performed by the processor156may cause the visual content to appear natural (e.g., appear as seen by a person viewing the location corresponding to the field of view of the capture devices102a-102n). In an example, the dewarping may alter the video data to generate a rectilinear video frame (e.g., correct artifacts caused by the lens characteristics of the lenses150a-150n). The dewarping operations may be implemented to correct the distortion caused by the lenses150a-150n. The adjusted visual content may be generated to enable more accurate and/or reliable object detection.

Various features (e.g., dewarping, digitally zooming, cropping, etc.) may be implemented in the processor156as hardware modules. Implementing hardware modules may increase the video processing speed of the processor156(e.g., faster than a software implementation). The hardware implementation may enable the video to be processed while reducing an amount of delay. The hardware components used may be varied according to the design criteria of a particular implementation.

The processor156is shown comprising a number of blocks (or circuits)190a-190n. The blocks190a-190nmay implement various hardware modules implemented by the processor156. The hardware modules190a-190nmay be configured to provide various hardware components to implement a video processing pipeline. The circuits190a-190nmay be configured to receive the pixel data PIXELD_A-PIXELD_N, generate the video frames from the pixel data, perform various operations on the video frames (e.g., de-warping, rolling shutter correction, cropping, upscaling, image stabilization, etc.), prepare the video frames for communication to external hardware (e.g., encoding, packetizing, color correcting, etc.), parse feature sets, implement various operations for computer vision (e.g., object detection, segmentation, classification, etc.), etc. Various implementations of the processor156may not necessarily utilize all the features of the hardware modules190a-190n. The features and/or functionality of the hardware modules190a-190nmay be varied according to the design criteria of a particular implementation. Details of the hardware modules190a-190nmay be described in association with U.S. patent application Ser. No. 16/831,549, filed on Apr. 16, 2020, U.S. patent application Ser. No. 16/288,922, filed on Feb. 28, 2019 and U.S. patent application Ser. No. 15/593,493 (now U.S. Pat. No. 10,437,600), filed on May 12, 2017, appropriate portions of which are hereby incorporated by reference in their entirety.

The hardware modules190a-190nmay be implemented as dedicated hardware modules. Implementing various functionality of the processor156using the dedicated hardware modules190a-190nmay enable the processor156to be highly optimized and/or customized to limit power consumption, reduce heat generation and/or increase processing speed compared to software implementations. The hardware modules190a-190nmay be customizable and/or programmable to implement multiple types of operations. Implementing the dedicated hardware modules190a-190nmay enable the hardware used to perform each type of calculation to be optimized for speed and/or efficiency. For example, the hardware modules190a-190nmay implement a number of relatively simple operations that are used frequently in computer vision operations that, together, may enable the computer vision algorithm to be performed in real-time. The video pipeline may be configured to recognize objects. Objects may be recognized by interpreting numerical and/or symbolic information to determine that the visual data represents a particular type of object and/or feature. For example, the number of pixels and/or the colors of the pixels of the video data may be used to recognize portions of the video data as objects.

One of the hardware modules190a-190n(e.g.,190a) may implement a scheduler circuit. The scheduler circuit190amay be configured to store a directed acyclic graph (DAG). In an example, the scheduler circuit190amay be configured to generate and store the directed acyclic graph in response to the feature set information received in the signal FEAT_SET. The directed acyclic graph may define the video operations to perform for extracting the data120a-120nfrom the video frames. For example, the directed acyclic graph may define various mathematical weighting to apply when performing computer vision operations to classify various groups of pixels as particular objects.

The scheduler circuit190amay be configured to parse the acyclic graph to generate various operators. The operators may be scheduled by the scheduler circuit190ain one or more of the other hardware modules190a-190n. For example, one or more of the hardware modules190a-190nmay implement hardware engines configured to perform specific tasks (e.g., hardware engines designed to perform particular mathematical operations that are repeatedly used to perform computer vision operations). The scheduler circuit190amay schedule the operators based on when the operators may be ready to be processed by the hardware engines190a-190n.

The scheduler circuit190amay time multiplex the tasks to the hardware modules190a-190nbased on the availability of the hardware modules190a-190nto perform the work. The scheduler circuit190amay parse the directed acyclic graph into one or more data flows. Each data flow may include one or more operators. Once the directed acyclic graph is parsed, the scheduler circuit190amay allocate the data flows/operators to the hardware engines190a-190nand send the relevant operator configuration information to start the operators.

Each directed acyclic graph binary representation may be an ordered traversal of a directed acyclic graph with descriptors and operators interleaved based on data dependencies. The descriptors generally provide registers that link data buffers to specific operands in dependent operators. In various embodiments, an operator may not appear in the directed acyclic graph representation until all dependent descriptors are declared for the operands.

One of the hardware modules190a-190n(e.g.,190b) may implement a convolutional neural network (CNN) module. The CNN module190bmay be configured to perform the computer vision operations on the video frames. The CNN module190bmay be configured to implement recognition of objects through multiple layers of feature detection. The CNN module190bmay be configured to calculate descriptors based on the feature detection performed. The descriptors may enable the processor156to determine a likelihood that pixels of the video frames correspond to particular objects (e.g., a particular make/model/year of a vehicle, etc.). The CNN module190bmay be configured to implement convolutional neural network capabilities. The CNN module190bmay be configured to implement computer vision using deep learning techniques. The CNN module190bmay be configured to implement pattern and/or image recognition using a training process through multiple layers of feature-detection. The CNN module190bmay be configured to conduct inferences against a machine learning model.

The CNN module190bmay be configured to perform feature extraction and/or matching solely in hardware. Feature points typically represent interesting areas in the video frames (e.g., corners, edges, etc.). By tracking the feature points temporally, an estimate of ego-motion of the capturing platform or a motion model of observed objects in the scene may be generated. In order to track the feature points, a matching algorithm is generally incorporated by hardware in the CNN module190bto find the most probable correspondences between feature points in a reference video frame and a target video frame. In a process to match pairs of reference and target feature points, each feature point may be represented by a descriptor (e.g., image patch, SIFT, BRIEF, ORB, FREAK, etc.). Implementing the CNN module190busing dedicated hardware circuitry may enable calculating descriptor matching distances in real time.

The CNN module190bmay be a dedicated hardware module configured to perform feature detection of the video frames. The features detected by the CNN module190bmay be used to calculate descriptors. The CNN module190bmay determine a likelihood that pixels in the video frames belong to a particular object and/or objects in response to the descriptors. For example, using the descriptors, the CNN module190bmay determine a likelihood that pixels correspond to a particular object (e.g., a person, an item of furniture, a pet, a vehicle, etc.) and/or characteristics of the object (e.g., a hood of a vehicle, a door of a vehicle, a logo of a vehicle, a license plate of a vehicle, a face of a person, clothing worn by a person, etc.). Implementing the CNN module190bas a dedicated hardware module of the processor156may enable the apparatus100to perform the computer vision operations locally (e.g., on-chip) without relying on processing capabilities of a remote device (e.g., communicating data to a cloud computing service).

The computer vision operations performed by the CNN module190bmay be configured to perform the feature detection on the video frames in order to generate the descriptors. The CNN module190bmay perform the object detection to determine regions of the video frame that have a high likelihood of matching the particular object. In one example, the types of object to match against (e.g., reference objects) may be customized using an open operand stack (enabling programmability of the processor156to implement various directed acyclic graphs each providing instructions for performing various types of object detection).

The CNN module190bmay be configured to perform local masking to the region with the high likelihood of matching the particular object(s) to detect the object.

In some embodiments, the CNN module190bmay determine the position (e.g., 3D coordinates and/or location coordinates) of various features of the detected objects. In one example, the location of the arms, legs, chest and/or eyes of a person and/or a hood, a roof, a driver side door and/or a battery location may be determined using 3D coordinates. One location coordinate on a first axis for a vertical location of the body part in 3D space and another coordinate on a second axis for a horizontal location of the body part in 3D space may be stored. In some embodiments, the distance from the lenses150a-150nmay represent one coordinate (e.g., a location coordinate on a third axis) for a depth location of the body part in 3D space. Using the location of various body parts in 3D space, the processor156may determine body position, and/or body characteristics of detected people.

The CNN module190bmay be pre-trained (e.g., configured to perform computer vision to detect objects based on the training data received to train the CNN module190b). For example, the results of training data (e.g., a machine learning model) may be pre-programmed and/or loaded into the processor156. The CNN module190bmay conduct inferences against the machine learning model (e.g., to perform object detection). The training may comprise determining weight values for each of the layers. For example, weight values may be determined for each of the layers for feature extraction (e.g., a convolutional layer) and/or for classification (e.g., a fully connected layer). The weight values learned by the CNN module190bmay be varied according to the design criteria of a particular implementation.

The convolution operation may comprise sliding a feature detection window along the layers while performing calculations (e.g., matrix operations). The feature detection window may apply a filter to pixels and/or extract features associated with each layer. The feature detection window may be applied to a pixel and a number of surrounding pixels. In an example, the layers may be represented as a matrix of values representing pixels and/or features of one of the layers and the filter applied by the feature detection window may be represented as a matrix. The convolution operation may apply a matrix multiplication between the region of the current layer covered by the feature detection window. The convolution operation may slide the feature detection window along regions of the layers to generate a result representing each region. The size of the region, the type of operations applied by the filters and/or the number of layers may be varied according to the design criteria of a particular implementation.

Using the convolution operations, the CNN module190bmay compute multiple features for pixels of an input image in each extraction step. For example, each of the layers may receive inputs from a set of features located in a small neighborhood (e.g., region) of the previous layer (e.g., a local receptive field). The convolution operations may extract elementary visual features (e.g., such as oriented edges, end-points, corners, etc.), which are then combined by higher layers. Since the feature extraction window operates on a pixel and nearby pixels, the results of the operation may have location invariance. The layers may comprise convolution layers, pooling layers, non-linear layers and/or fully connected layers. In an example, the convolution operations may learn to detect edges from raw pixels (e.g., a first layer), then use the feature from the previous layer (e.g., the detected edges) to detect shapes in a next layer and then use the shapes to detect higher-level features (e.g., facial features, pets, vehicles, components of a vehicle, furniture, etc.) in higher layers and the last layer may be a classifier that uses the higher level features.

The CNN module190bmay execute a data flow directed to feature extraction and matching, including two-stage detection, a warping operator, component operators that manipulate lists of components (e.g., components may be regions of a vector that share a common attribute and may be grouped together with a bounding box), a matrix inversion operator, a dot product operator, a convolution operator, conditional operators (e.g., multiplex and demultiplex), a remapping operator, a minimum-maximum-reduction operator, a pooling operator, a non-minimum, non-maximum suppression operator, a scanning-window based non-maximum suppression operator, a gather operator, a scatter operator, a statistics operator, a classifier operator, an integral image operator, comparison operators, indexing operators, a pattern matching operator, a feature extraction operator, a feature detection operator, a two-stage object detection operator, a score generating operator, a block reduction operator, and an upsample operator. The types of operations performed by the CNN module190bto extract features from the training data may be varied according to the design criteria of a particular implementation.

Each of the hardware modules190a-190nmay implement a processing resource (or hardware resource or hardware engine). The hardware engines190a-190nmay be operational to perform specific processing tasks. In some configurations, the hardware engines190a-190nmay operate in parallel and independent of each other. In other configurations, the hardware engines190a-190nmay operate collectively among each other to perform allocated tasks. One or more of the hardware engines190a-190nmay be homogenous processing resources (all circuits190a-190nmay have the same capabilities) or heterogeneous processing resources (two or more circuits190a-190nmay have different capabilities). Referring toFIG. 3, a block diagram illustrating components for performing sensor fusion to make a decision about a temperature anomaly is shown. The processor156and the memory158are shown. The signal DATA is shown communicated between the processor156and the memory158. The processor156is shown receiving the signal PIXELD_I and generating the signal ANOM.

The processor156is shown comprising the CNN module190b, a block (or circuit)200a block (or circuit)202and/or a block (or circuit)204. The circuit200may implement a video processing pipeline. The circuit202may implement a sensor fusion module. The circuit204may implement a decision module. The processor156may comprise other components (not shown). The components of the processor156shown in association withFIG. 3may generally correspond to components for combining information generated in response to the computer vision operations and the information generated by the thermal sensor104to provide the object-aware temperature anomaly monitoring.

The memory158is shown comprising a block (or circuit)210. The block210may comprise data storage for object classifications. The object classifications210may comprise a block (or circuit)212and/or a block (or circuit)214. The block212may comprise data storage for operating temperatures. The block214may comprise data storage for anomaly location. The object classifications210, the operating temperatures212and/or the anomaly locations214may generally comprise look-up tables.

The object classifications210may comprise other types of data storage (not shown). The types of data stored for the object classifications210may be varied according to the design criteria of a particular implementation. The components of the memory158shown in association withFIG. 3may generally correspond to components for combining information generated in response to the computer vision operations and the information generated by the thermal sensor104to provide the object-aware temperature anomalies.

The video processing pipeline200is shown receiving the signal PIXELD_I (e.g., generated by the capture device102). In the example shown, only the signal PIXELD_I is shown. However, the video processing pipeline200may be configured to receive any number of the pixel data signals PIXELD_A-PIXELD_N. The video processing pipeline200is shown generating a signal (e.g., VFRAMES). The signal VFRAMES may comprise video frames generated by the video processing pipeline. The signal VFRAMES may be presented to the CNN module190b.

The video processing pipeline200may be configured to perform video processing on the pixel data received from the capture devices102a-102n. The video processing performed by the video processing pipeline200may be configured to generate the video frames from the pixel data. In one example, the video frames generated by the video processing pipeline200may be used internally by other components of the processor156(e.g., for computer vision operations). In another example, the video frames generated by the video processing pipeline200may be streamed to another device (e.g., the communication device154may communicate the signal VFRAMES). In yet another example, the video frames generated by the video processing pipeline200may be provided to a display device (e.g., a monitor). In the example shown, the signal VFRAMES may communicate the video frames generated by the video processing pipeline200to the CNN module190bto perform computer vision operations on the video frames.

The video pipeline200may be configured to encode video data and/or video frames captured by each of the capture devices102a-102n. In some embodiments, the video pipeline200may be configured to perform video stitching operations to stitch the pixel data PIXELD_A-PIXELD_N captured by each of the capture devices102a-102nusing the lenses112a-112nto generate a panoramic field of view (e.g., the panoramic video frames). The video pipeline200may be configured to generate the video frames VFRAMES and perform further operations on the video frames VFRAMES. The video pipeline200may be configured to perform de-warping, cropping, enhancements, rolling shutter corrections, stabilizing (e.g., electronic image stabilization (EIS)), downscaling, packetizing, compression, conversion, blending, synchronizing and/or other video operations. The architecture of the video pipeline200may enable the video operations to be performed on high resolution video and/or high bitrate video data in real-time and/or near real-time. The video pipeline module200may enable computer vision processing on 4K resolution video data, stereo vision processing, object detection, 3D noise reduction, fisheye lens correction (e.g., real time 360-degree dewarping and lens distortion correction), oversampling and/or high dynamic range processing. In one example, the architecture of the video pipeline200may enable 4K ultra high resolution with H.264 encoding at double real time speed (e.g., 60 fps), 4K ultra high resolution with H.265/HEVC at 30 fps, 4K AVC encoding and/or other types of encoding (e.g., VP8, VP9, AV1, etc.). The video data generated by the video pipeline module200may be compressed (e.g., using a lossless compression and/or a low amount of lossiness). The type of video operations and/or the type of video data operated on by the video pipeline200may be varied according to the design criteria of a particular implementation.

The video pipeline module200may implement a digital signal processing (DSP) module configured to receive information (e.g., pixel data values captured by the sensors182a-182n) from the input signals PIXELD_A-PIXELD_N. The video pipeline module200may be configured to determine the pixel values (e.g., RGB, YUV, luminance, chrominance, etc.). The video pipeline module200may be configured to perform image signal processing (ISP). The video pipeline module200may be further configured to support or provide a sensor RGB to YUV raw image pipeline to improve image quality, perform bad pixel detection and correction, demosaicing, white balance, color and tone correction, gamma correction, adjustment of hue, saturation, brightness and contrast adjustment, sharpening and/or chrominance and luminance noise filtering.

The video pipeline module200may encode the raw image data into a plurality of encoded video streams simultaneously (in parallel). The plurality of video streams may have a variety of resolutions (e.g., VGA, WVGA, QVGA, SD, HD, Ultra HD, 4K, 8K, etc.). The video pipeline module200may receive encoded and/or unencoded (e.g., raw) audio data from an audio interface. The video pipeline module200may also receive encoded audio data from a communication interface (e.g., USB and/or SDIO via the signal DIR_AUD). The video pipeline module200may provide encoded video data to the communication devices154(e.g., using a USB host interface) and/or displays.

The video pipeline module200may be configured to implement a raw image pipeline for image signal processing. The video pipeline module200may be configured to convert image data acquired from the capture devices102a-102n. For example, the image data may be acquired from the image sensor182in a color filter array (CFA) picture format. The raw image pipeline implemented by the video pipeline module200may be configured to convert the CFA picture format to a YUV picture format.

The raw image pipeline implemented by the video pipeline module200may be configured to perform demosaicing on the CFA formatted image data to obtain linear RGB (red, green, blue) image data for each picture element (e.g., pixel). The raw image pipeline implemented by the video pipeline module200may be configured to perform a white balancing operation and/or color and tone correction. The raw image pipeline implemented by the video pipeline module200may be configured to perform RGB to YUV color space conversion. The raw image pipeline implemented by the video pipeline module200may be configured to perform noise filtering (e.g., noise reduction, noise correction, etc.) and/or sharpening. The raw image pipeline implemented by the video pipeline module200may be configured to implement tone based non-smoothness detection and adjustment. Generally, noise filtering may be performed after each step, operation, and/or conversion performed to reduce any noise introduced by each step.

The video pipeline module200may implement scheduling. Scheduling may enable the video pipeline200to perform various discrete, asynchronous video operations and/or computer vision operations in parallel. The scheduling may enable data results from one video operation to be available by the time another video data operation needs the data results. The video pipeline module200may comprise multiple pipelines, each tuned to perform a particular task efficiently. For example, each of the multiple pipelines may utilize one or more of the dedicated hardware modules190a-190n.

The CNN module190bis shown receiving the signal VFRAMES from the video processing pipeline200. The CNN module190bmay generate a signal (e.g., OBJ). The signal OBJ may be presented to the sensor fusion module202. The signal OBJ may be presented to the object classifications storage210.

The CNN module190bmay be configured to perform the computer vision operations on the video frames. The computer vision operations may be performed in response to the signal VFRAMES received from the video processing pipeline200. The CNN module190bmay be configured to detect objects, classify objects and/or determine characteristics or features of the detected objects. The CNN module190bmay generate the signal OBJ in response to detecting the objects in the video frames.

The CNN module190bmay be configured to perform the computer vision operations. The computer vision operations may comprise segmentation, object detection and/or classification of the video frames VFRAMES. The video processing pipeline200may be configured to perform video processing. The video processing performed by the video processing pipeline200may be a distinct process from the computer vision operations. The video processing may be configured to generate the video frames VFRAMES that may be used for the computer vision operations. The video processing operations may generate encoded video frames that may be output to a display and unencoded video frames that may be used for the computer vision operations.

The object classifications storage210may comprise one or more lookup tables. In an example, the operating temperatures storage212and/or the anomaly location storage214may each implement lookup tables. The objects classifications storage210may be configured to provide data in response to the signal OBJ. In one example, the CNN module190bmay be configured to classify the objects in the video frames VFRAMES and provide the signal OBJ to the objects classifications lookup data210. The objects classifications lookup data210may generate data that corresponds to the objects classified by the CNN module190b. In another example, the CNN module190bmay be configured to detect various features in the video frames VFRAMES and the objects classifications lookup data210may be compared to the features detected to classify the objects as particular types/classes of object and then generate the data that corresponds to the objects classified.

The objects classifications storage210may comprise data that corresponds to particular types (e.g., classes) of objects. The data may comprise the operating temperatures212. In some embodiments, the operating temperatures212may be configured to store information about normal operating temperature ranges of particular objects. The normal operating temperature ranges may comprise a minimum and/or maximum temperature values for particular objects and/or features of objects. In an example, the normal operating temperature ranges may be a recommended and/or safe operating temperature (e.g., as suggested by a manufacturer and/or regulator of the object/feature). In one example, one object classification may be particular model of electric vehicle that may have a normal operating temperature range of −20 C to 60 C. In some embodiments, the operating temperatures212may be configured to store information about changes in temperature. In one example, one object classification may be a particular model of electric vehicle that may change temperature over time, but may indicate that an anomalous temperature change may be a rapid change in temperature (e.g., 10 degree increase within a minute). Each class of object may have a corresponding operating temperature stored in the operating temperatures lookup table212. The particular ranges of temperatures considered normal and/or anomalous may be varied according to the design criteria of a particular implementation.

The objects classifications storage210may comprise data that corresponds to particular features and/or characteristics of objects. The data may comprise the anomaly location data214. The anomaly location data214may comprise locations of particular types of classes of objects that may be likely to have a temperature anomaly (e.g., areas of potential concern). The anomaly location214may comprise information about particular types of materials used by the object class (e.g., combustible materials) and/or where the particular materials are located on the class of objects. The anomaly location data214may store data that may be used to distinguish which high temperature detections correspond to false alarms and/or which high temperature detections correspond to temperature anomalies. In one example, the feature and/or characteristic of the object class stored by the anomaly location data214may be a battery (e.g., for an electric vehicle, for a battery storage unit, etc.). In another example, the feature and/or characteristic of the object class stored by the anomaly location data214may be a gas tank (e.g., for a vehicle with an internal combustion engine). In yet another example, the feature and/or characteristic of the object class stored by the anomaly location data214may be other components of a vehicle and/or the temperatures associated with the particular component (e.g., the roof may heat up to 70 C, the hood may heat up to 80 C, the interior of the vehicle may heat up to 60 C, etc.). The types of features and/or the temperatures associated with each feature may be varied according to the design criteria of a particular implementation.

In some embodiments, temperature anomalies may be associated with particular locations of a class of object. In one example, a class of object detected may be a vehicle. A high temperature may be detected by the thermal sensor104on the roof of the detected vehicle. However, when outdoors the sun may cause the roof of a vehicle to become very hot, which may be a benign, expected and generally safe increase in temperature. In another example, a class of object detected may be an EV and the anomaly location214may indicate that the EV battery may be located on a bottom of the EV. A rapid temperature increase on the bottom of the EV (e.g., the location that corresponds to the anomaly location data214) may indicate a temperature anomaly and/or a potential fire hazard. The operating temperatures212may be associated with the anomaly locations214. For example, a temperature above 60 C may be considered normal and/or acceptable for the roof of a vehicle, but may be considered a temperature anomaly for the location of a car battery.

The object classifications data210may be configured to provide information about operating temperatures and/or locations of particular features of particular classes of objects to the processor156. The information generated by the objects classifications data210may be communicated using the signal DATA. In one example, the CNN module190bmay detect a particular make/model of electric vehicle. The object classifications210may be used to look up the operating temperatures212for the particular make/model of electric vehicle. The anomaly location214may be used to determine the location of the EV battery. The operating temperature range and/or the location of various features of the electric vehicle, such as the battery, may be communicated to the processor156.

The sensor fusion module202may be configured to analyze various sources of data simultaneously. The sensor fusion module202may receive the signal OBJ from the CNN module190band the signal THIMG from the thermal sensor104. The sensor fusion module202may be configured to analyze data and/or intelligently combine results from the image data (e.g., the video frames VFRAMES and/or the objects detected OBJ) and the thermal image (e.g., the temperature measurements performed by the thermal sensor104). The sensor fusion module202may be further configured to use the information from the object classifications lookup table210. The information from the object classifications lookup table210may be used to analyze data with respect to the particular class of object detected.

The sensor fusion module202may be configured to make inferences based on the combination of data sources. The sensor fusion module202may be configured to generate a signal (e.g., INF). The signal INF may comprise the inferences made by the sensor fusion module202. The signal INF may be presented to the decision module204.

The sensor fusion module202may be configured to analyze information from the capture devices102a-102n(e.g., RBG image data), the thermal sensor104(e.g., a thermal image) and/or the object classifications data210. By analyzing various data from disparate sources, the sensor fusion module202may be capable of making inferences about the data that may not be possible from one of the data sources alone. For example, the sensor fusion module202may analyze video data as well as radar, lidar, inertial, motion, V2X, location data (e.g., GPS, GNSS, ADAS, etc.), thermal data and/or other sources to develop a model of a scenario to support decision making. The sensor fusion module202may also provide time correlation, spatial correlation and/or reliability among the data being received by the processor156.

In an example, the sensor fusion module202may spatially overlay an object captured by the camera102iwith the same object captured by the thermal sensor104for better identification and/or localization of a temperature for a detected object. In a time correlation example, an object may be seen by two sensors (e.g., RGB camera and thermal) at slightly different times and/or slightly different angles. The sensor fusion module202may time shift the data from one sensor to align with the data from the other sensor (e.g., using the disparity calculation based on the locations and/or mounting information of each sensor).

In a reliability example, the sensor fusion module202may determine the reliability of objects detected by each sensor.

The sensor fusion module202may adjust the weighting used to overlay the data to give more weight to reliable data and/or less weight to unreliable data (e.g., one of the capture devices102a-102nmay have low reliability in foggy conditions, but thermal data may still have good reliability in foggy conditions). A confidence that the object is really there and is correctly identified may also be calculated in the sensor fusion module202.

The sensor fusion module202may aggregate data from the thermal sensor104, the microphones160a-160n, the CNN module190band/or the video pipeline200to build a model and/or abstraction of the environment around the detected objects. The computer vision operations may enable the processor156to understand the environment, a state of objects, relative positions of objects and/or a meaning of objects to derive inferences (e.g., detect that a vehicle is in the shade, detect that a vehicle is in direct sunlight, detect that a vehicle has been recently been driven and may have a higher temperature, understand that a vehicle is unattended, etc.). The sensor fusion module202may enable a comparison and/or cross-reference of the data received from the thermal sensor104at a particular time to the video data captured at another particular time in order to adjust a confidence level of an inference. The type of inferences made by the sensor fusion module202may be varied according to the design criteria of a particular implementation.

The sensor fusion module202may be further configured to mathematically weight the information received from the computer vision operations (e.g., modify coefficients to represent how likely the detections made by the computer vision operations are correct based on the detections made based on other sensors). For example, the sensor fusion module202may be configured to mathematically weight the information provided by each sensor (e.g., a confidence level of the computer vision detection, a confidence level of the detection of the thermal sensor104, the distance limitations of the thermal sensor104, whether the computer vision detects the object at a distance beyond the range of the thermal sensor104, etc.).

The decision module204may be configured to determine whether to generate the control/warning signal. The decision module204may analyze the inferences made by the sensor fusion module202. The decision module204may be configured to compare the inferences made by the sensor fusion module202to various policy data to determine whether a temperature anomaly has been detected. For example, the policy data may indicate whether to be more conservative (e.g., avoid generating false alarms) or more proactive (e.g., prefer generating warnings). The policy data may be a distinct aspect of the apparatus100from the computer vision, video processing and/or thermal imaging. The decision module204may receive the signal from the sensor fusion module202and generate the signal ANOM.

The decision module204may be configured to generate the signal ANOM to indicate a temperature anomaly has been detected. The signal ANOM may be used to initiate an early warning (e.g., to be communicated by the communication device154). The decision module204may be configured to use the information from the computer vision operations and/or the sensor fusion module202to determine which actions may be taken. For example, the decision module204may determine which object is associated with the detected temperature anomaly. The decision module204may utilize data from the CNN module190band/or computer vision data using a histogram oriented gradient (HOG). The sources of data for making decisions used by the decision module204may be varied according to the design criteria of a particular implementation.

Referring toFIG. 4, a diagram illustrating example classification data is shown. The object classification data210, the operating temperature data212and/or the anomaly location data214are shown in a tabular format as a representative example of the lookup table data. The format of the lookup table and/or the arrangement of the object classification data210, the operating temperature data212, the anomaly location data214in the memory158and/or other data stored in the memory158may be varied according to the design criteria of a particular implementation. Object types250a-250nare shown. The object types250a-250nare shown as representative examples. The object type250amay have a classification210of EV model A (e.g., one make/model of an electric vehicle). The object type250bmay have a classification210of EV model B (e.g., another make/model of an electric vehicle). The object type250nmay have a classification210of a power storage unit (e.g., a large battery pack used to store power and supply power to a building, a charging station, etc.). The classification210may be a broad classification (e.g., detecting an electric vehicle), a relatively narrower classification (e.g., detecting a make/model of electric vehicle) and/or a specific classification (e.g., detecting a particular vehicle (e.g., using a license plate and/or other identifiable features to distinguish between individual vehicles of the same make/model)).

The object types250a-250nare shown having an operating temperature212and an anomaly location214. In the example shown, the object type250amay have an operating temperature212of −20 C to 60 C and an anomaly location214of a front bottom of the vehicle (e.g., the EV battery may be located at the bottom of the vehicle at the front end). In the example shown, the object type250bmay have an operating temperature212of −10 C to 55 C and an anomaly location214of a rear bottom of the vehicle (e.g., the EV battery may be located at the bottom of the vehicle at the rear end). In the example shown, the object type250cmay have an operating temperature212of −25 C to 65 C and an anomaly location214of the entire unit (e.g., the entire power supply may be a large battery pack).

The camera system100may be configured to detect the temperature anomaly based on the specific class of the objects detected. In the example shown, each of the object types250a-250nmay have similar but different normal operating temperatures and/or anomaly locations. Since the object types250a-250nmay have different normal operating and/or anomaly locations, the criteria analyzed by the camera system100to determine whether a temperature anomaly has been detected may be different for each object. For example, if a temperature of 59 C is detected for the object type250b, then the decision module204may generate the signal ANOM. However, the same temperature of 59 C detected for the object type250aand/or the object type250nmay not be considered a temperature anomaly. In another example, if a temperature of 70 C is detected on the front bottom of the object type250a, then the decision module204may generate the signal ANOM (e.g., based on the location of the anomaly location214). However, the same temperature of 70 C detected on the front bottom of the object type250bmay not be considered a temperature anomaly (e.g., the anomaly location214for the object type250bmay be the rear bottom and not the front bottom).

In the example shown, the operating temperature data212is shown as temperature ranges. Generally, for generating the signal ALERT for a fire hazard, the lower bound of the temperature range may not be relevant. For example, the processor156may determine whether the temperature measurement performed by the thermal sensor104has exceeded the upper bound of the temperature range (e.g., a temperature measurement greater than 60 C for the object type250a).

In some embodiments, the operating temperature data212may comprise one or both of the temperature range and a temperature rate of change. The temperature rate of change may comprise a threshold for how quickly the temperature may increase over a particular period of time. In one example, the operating temperature212for one or more of the types of objects250a-250nmay be a rate of change of 10 C over one minute. Each of the object types250a-250nmay have a specific rate of change. For example, the processor156may detect a temperature anomaly for the object type250ain response to detecting a temperature measurement by the thermal sensor104of either greater than 60 C or a rate of change in temperature of 10 C over a one minute period of time. The particular temperature ranges and/or temperature rates of change that may correspond to a temperature anomaly may be varied according to the design criteria of a particular implementation.

The camera system100may be configured to determine whether temperature anomalies have occurred specific to particular classes of objects detected. The camera system100may be configured to determine that a temperature anomaly has occurred based on general criteria. The general criteria may be the policy data implemented by the decision module204. In an example, the processor156may determine that a temperature anomaly has occurred if a temperature of 120 C has been detected regardless of the class of object detected. The general criteria may be relied on as fallback data in the event that a particular class of object has been misclassified by the CNN module190b.

Referring toFIG. 5, a diagram illustrating an example thermal image is shown. A thermal image300is shown. The thermal image300may be a representative example of a thermal image generated by the thermal sensor104. In an example, the thermal image300may be part of the data in the signal THIMG communicated to the processor156.

The thermal image300may comprise various regions of temperature measurements performed by the thermal sensor104. The thermal image300may provide a low resolution of data. For example, the low resolution of data may provide temperature measurements of areas that may provide a general visual representation of the thermal monitoring region112a-112b. However, the low resolution data may not provide a sufficient level of detail to identify specific objects and/or identify particular characteristics of an object. The low resolution data of the thermal image may be insufficient for computer vision operations. The vehicle60ais shown in the thermal image300. For illustrative purposes, only temperature regions associated with the vehicle60aare shown. However, the thermal image300may generally comprise temperature regions throughout the entire thermal image300.

The vehicle60ais shown in the example thermal image300. The resolution of the data in the signal THIMG may not be sufficient to indicate the presence of the vehicle60a. With the data in the thermal image300alone, the presence of the vehicle60amay not be known. The thermal image300may provide regions of temperature measurements without providing data about the objects in the thermal image300. The sensor fusion module202may be configured to infer the types of objects in the thermal image300based on the temperature regions. However, the objects and/or classification of the objects may be determined in response to the computer vision operations performed on the video frames VFRAMES (e.g., the RBG images).

A temperature measurement region302, temperature measurement regions304a-304band/or temperature measurement regions306a-306bare shown on the vehicle60a. The temperature measurement regions302-306bmay represent temperature measurements performed by the thermal sensor104. The temperature measurement regions302-306bmay represent regions of the vehicle60athat have similar temperatures. For example, areas of the vehicle60awith the temperature region302may have a similar temperature, areas of the vehicle60awith the temperature regions304a-304bmay have a similar temperature and areas of the vehicle60awith the temperature regions306a-306bmay have a similar temperature. However, the temperature of the temperature region302may be different from the temperature of the temperature regions304a-304b, which may both be different from the temperature of the temperature regions306a-306b. The temperature regions302-306bshown on the vehicle60amay be different temperatures than temperature regions that do not correspond to the vehicle60a(not shown). The particular temperature of the various temperature regions302-306bmay be varied according to the design criteria of a particular implementation.

The temperature measurement region302may be represented by the unshaded areas of the vehicle60a. The temperature measurement region302may represent areas of the vehicle60athat have a lowest (e.g., coolest) temperature measurement read by the thermal sensor104. For example, the temperature measurement region302may comprise the rear end and driver side door of the vehicle60a.

The temperature measurement regions304a-304bmay be represented by the areas of the vehicle60awith linear hatching. The temperature measurement regions304a-304bmay have a higher temperature (e.g., hotter) than the temperature region302. The temperature measurement regions304a-304bmay be temperature measurements within the normal operating temperature range212for the class of the vehicle60a. In one example, the temperature measurement regions304a-304bmay have a relatively stable temperature (e.g., not increasing). In the example shown, the temperature measurement region304amay correspond to the roof of the vehicle60aand the temperature measurement region304bmay correspond to the hood of the vehicle60a. For example, direct sunlight shining on the roof and hood of the vehicle60amay be the cause of the increased temperature in the temperature measurement regions304a-304b.

The temperature measurement regions306a-306bmay be represented by the areas of the vehicle60awith crosshatching. The temperature measurement regions306a-306bmay have a highest temperature (e.g., hottest). In one example, the temperature measurement regions306a-306bmay be temperature measurements above the normal operating temperature range212for the class of the vehicle60a. In another example, the temperature measurement regions306a-306bmay be areas that are rapidly increasing in temperature. In the example shown, the temperature measurement region306amay correspond to the front bottom of the vehicle60aand the temperature measurement region306bmay correspond to the bottom of the vehicle60a. For example, EV battery of the vehicle60amay be the cause of the temperature anomaly in the temperature measurement regions306a-306b.

The temperature measurement regions304a-304band/or the temperature measurement regions306a-306bmay not exactly cover the vehicle60a. For example, the temperature measurement region304ais shown extending beyond the roof of the vehicle60a. In another example, the temperature measurement region306bis shown extending below the bottom of the vehicle60a. Since the temperature measurements performed by the thermal sensor104does not determine the objects in the thermal image300, the temperature measurements may not exactly correspond to the location of the vehicle60ain the thermal image300. Heat may be emitted and/or radiate from surfaces of objects. The radiating heat may be detected the thermal sensor104. Without the addition of computer vision operations performed by the processor156, the thermal measurements302-306bmay not accurately indicate the shape and/or location of an object.

The thermal sensor104may be configured to continually monitor the thermal monitoring region112a-112b. The thermal sensor104may provide the temperature measurements302-306bover time to generate a temperature curve (e.g., a long term analysis over a number of hours). The thermal sensor104may be configured to provide the temperature measurements302-306bas short term measurements (e.g., a temperature curve over a range of a few minutes in order to detect rapid temperature increases).

Using the temperature measurements302-306bin the thermal image300alone to determine the presence of a temperature anomaly may result in false positives. In the example shown, two regions306a-306bmay have a very high temperature. However, only one (or none) may correspond to a feature of the vehicle60athat may result in a hazard (e.g., a fire hazard). Without knowledge of the class of the detected object determined from the computer vision operations, which of the normal operating temperature data212to use to determine whether there is a temperature anomaly may not be known.

Referring toFIG. 6, a diagram illustrating object detection and/or classification is shown. An example video frame350is shown. The example video frame350may be one of the video frames VFRAMES. The video frame350may be generated in response to one or more of the capture devices102a-102n. For example, the video frame350may comprise a view corresponding to the region of interest110a-110b. For example, the capture devices102a-102nmay generate the signals PIXELD_A-PIXELD_N (e.g., pixel data), the video processing pipeline200of the processor156may generate the video frame350in response to the pixel data (e.g., the video processing operations). In the example shown, the video frame350may provide a different example scenario than the thermal image300shown in association withFIG. 5. The different scenarios for the thermal image300and the video frame350are shown for illustrative purposes. Generally, the thermal image300and the video frame350captured by the same camera system100may provide a view of the same scenario from generally the same perspective. The video frame350may represent an example RBG image.

The video frame350may provide a high resolution of data. For example, the high resolution of data may provide details for a visual representation of the region of interest110a-110b. In an example, the high resolution of data may be a 1080p resolution, a 1440p resolution, a 4K resolution, an 8K resolution, etc. The high resolution data may provide a sufficient level of detail to identify specific objects, classify objects and/or identify particular characteristics of an object. While, the high resolution data of the video frame350may provide a sufficient amount of data for computer vision operations, the visual data of the video frame350may not provide temperature data. The CNN module190bmay be configured to perform the computer vision operations on the video frame350. For example, the computer vision operations may be performed after the video processing operations have started (e.g., performed on the video frames as they are generated by the video processing operations).

The video frame350may comprise the ground52band the vehicles60a-60d. Parking lines352a-352bare shown on the ground52b. In the example shown, the vehicles60b-60dare shown parked within the parking lines352aand the vehicle60ais shown parked within the parking lines352b. For example, each of the vehicles60a-60dmay be unattended vehicles. The vehicle60ais shown comprising an identifier354and a license plate356. In an example, the identifier354may be a vehicle logo. In another example, the identifier354may be a sticker, a decal, a dent, a scratch, etc.

Dotted boxes360a-360dare shown. The dotted boxes360a-360dare each shown around a respective one of the vehicles60a-60d. The dotted boxes360a-360dmay represent the object detection (e.g., bounding boxes) of the computer vision operations performed by the CNN module190b. The dotted boxes360a-360dmay represent that the CNN module190bhas detected the respective vehicles60a-60das objects. While only the detected vehicles360a-360dare shown as detected objects as a representative example, the CNN module190bmay detect other objects (e.g., the parking lines352a-352b, pedestrians, cyclists, trees, street signs, billboards, etc.). The number and/or types of objects detected in each of the video frames VFRAMES may be varied according to the design criteria of a particular implementation.

The CNN module190bmay be configured to perform the object detection on the video frame350. The CNN module190bmay be configured to classify the detected objects360a-360d. In one example, the CNN module190bmay be configured to classify each of the vehicles60a-60das a vehicle object type. In some embodiments, the CNN module190bmay be configured to classify the vehicles60a-60das particular types of vehicles. In one example, the CNN module190bmay classify one or more of the vehicles broadly (e.g., a truck, a sedan, a SUV, a van, an internal combustion engine vehicle, an EV, etc.). In another example, the CNN module190bmay be configured to perform a fine-grained classification (e.g., identify a vehicle as a particular make/model/year). In yet another example, the CNN module190bmay be configured to perform a specific classification (e.g., identify the vehicle60aas a particular vehicle distinguishable from all other vehicles regardless of make/model/year). The level of classification performed by the CNN module190bmay be varied according to the design criteria of a particular implementation.

A dotted shape362and a dotted shape364are shown. The dotted shapes362-364may represent the analysis of characteristics of the detected object360a. In an example, the CNN module190bmay be configured to detect characteristics of the detected objects360a-360din order to perform the classification and/or to perform a fine-grained classification.

In one example, if the identifier354is a vehicle logo, the analysis of the characteristic362may comprise identifying the logo354(e.g., matching the detected logo354to a logo in a library of known logos). The logo354may be used to identify a particular make/model/year of the vehicle60a. The make/model/year may be compared to a library of known vehicles to indicate whether the vehicle60ais an EV. In another example, if the identifier354is a scratch, the analysis of the characteristic362may comprise identifying the scratch354. The scratch354may be used to identify the vehicle60aas a particular vehicle. For example, if the vehicle60aand the vehicle60bare the same make/model/year of vehicle, the scratch354may be used to distinguish the vehicle60afrom the vehicle60b.

In one example, detected characteristic364may be the license plate356. The CNN module190band/or the processor156may be configured to perform OCR to read the license plate356. By reading the license plate356, the processor156may distinguish the vehicle60aas a particular vehicle. In some embodiments, the communication device154may have access to communicate with a vehicle database (e.g., a remote source of data). For example, the communication device154may query the vehicle database using the data read from the license plate356. The result of the query of the vehicle database may provide the vehicle make/model/year and/or owner of the detected vehicle360a. The information from the vehicle database may help classify the detected vehicle360a. In some embodiments, the signal ALERT may be provided to the owner of the vehicle60abased on the license plate information (e.g., a personalized warning that a temperature anomaly has been detected). In some embodiments, the detected characteristic364of the license plate356may provide an indication of whether the vehicle60ais an electric vehicle or not (e.g., the license plate may indicate that the vehicle is an EV).

A station370is shown. In one example, the station370may be an electric charging station (e.g., for charging electric vehicles). In another example, the station370may be a re-fueling station (e.g., a gas station). In the example shown, the station370may be an electric charging station connected to the electric vehicles60c-60d. The electric charging station370may be providing a recharge to the electric vehicles60c-60d.

The camera system100′ is shown mounted to the electric charging station (e.g., the camera system100′ may not be the camera system100that captured the example video frame350). The camera system100′ may be configured similar to the camera system100. The camera system100′ may be configured to monitor for temperature anomalies of vehicles connected to the electric charging station370. For example, the camera system100′ may monitor the temperature of the vehicle60cand the vehicle60dto detect for indications of thermal runaway (e.g., rapidly increasing heat) while the vehicles60c-60dare being charged by the electric charging station370. In an example, in a parking lot scenario with multiple electric charging stations, each of the charging stations may comprise the camera system100configured to perform object-aware temperature monitoring of vehicles receiving power (e.g., recharging) from the electric charging stations.

In some embodiments, the charging station370may be attached to a pole and the camera system100′ may be mounted to the pole attached to the charging station370. The camera system100′ may monitor temperature and provide early warning of a potential fire hazard and also serve as a surveillance camera. For example, the CNN module190bmay be further configured to detect people and/or determine a behavior of a person and/or people detected (e.g., to detect car break-ins, vandalism, theft, etc.). The signal FEAT_SET may provide feature set data for detecting security surveillance issues in addition to the temperature monitoring. The signal ANOM may be used to provide early warnings for potential fire hazards and/or alarms when particular behavior is detected using computer vision (e.g., theft, vandalism, arson, etc.).

A dotted box372is shown. The dotted box372may represent the object detection and/or classification of the station370. In an example, the camera system100may detect the charging station370as an object and monitor for temperature anomalies that may occur in the charging station370. For example, the charging station370may be a power storage unit. The charging station370may be classified based on the object classifications210. The charging station370may have different normal operating temperatures212and/or anomaly locations214than the detected objects360a-360dthat have a different classification.

The detected objects360a-360d(and the characteristics362-364and the detected object370) may be localized on the video frame350. The processor156may be configured to determine where in the region of interest110a-110neach of the detected objects360a-360d(and the characteristics362-364and the detected object370) are located. The localization may enable the sensor fusion module202to compare the locations of the temperature measurement regions302-306bfrom the thermal image300to the locations of the detected objects360a-360d(and the characteristics362-364and the detected object370) in the visual image350.

The classification of the detected objects360a-360dmay be used to determine the normal operating temperatures for the detected objects360a-360d. The location of the detected objects360a-360d, the information about normal operating temperatures of the detected objects360a-360ddetermined from the classification and the data from the thermal image may be combined. The combination of the data may be used to determine whether a temperature anomaly has been detected and/or whether the early alert should be generated.

Referring toFIG. 7, a diagram illustrating a comparison of data from a thermal image to objects detected in a visual image is shown. A comparison frame400is shown. The comparison frame400may comprise the image of the vehicle60asimilar to the view shown in the thermal image300. The comparison frame400may be a representative example of a combination of the results determined from the thermal image300and the results of the object detection performed on the video frame350. The comparison frame400may comprise data analyzed by the sensor fusion module202. The sensor fusion module202may generate the comparison frame400in order to correlate the low resolution data from the thermal image300to the high resolution data from the video frame350.

The processor156may be configured to apply computer vision to the combination of the visible light imaging sensor102and the thermal imaging sensor104. The processor156may be configured to combine human visible imaging and thermal imaging. The computer vision techniques may be used on either the thermal image300, the video frame350and/or both using the sensor fusion module202. The computer vision techniques may provide object awareness for the temperatures measurements in the thermal image. The object awareness may provide an additional source of information for the sensor fusion module202to use along with the thermal image for detecting the temperature anomalies. Using multiple sources of information may prevent false alarms that may occur if relying on one source of information alone (e.g., temperature measurements alone).

Dotted boxes402a-402dare shown on the vehicle60a. The dotted boxes402a-402dmay represent various features of the detected object360a(e.g., the vehicle60a). The features402a-402dmay be detected by the CNN module190b. One or more of the features402a-402dmay generally correspond to the locations of the temperature measurement regions302-306bfrom the thermal image300. The number, location and/or type of features402a-402ddetected by the CNN module190bmay be varied according to the design criteria of a particular implementation.

In the example shown, the feature402amay be the roof of the vehicle60a, the feature402bmay be the hood of the vehicle60a, the feature402cmay be the front end of the vehicle60aand the feature402dmay be the battery of the vehicle60a. The sensor fusion module202may be configured to analyze the features402a-402din response to the object classifications210, the operating temperatures212and/or the anomaly locations214. In one example, the sensor fusion module202may be configured to use said objects detected to detect said features402a-402dto determine and/or detect potentially flammable components of an object (e.g., detect thermal runaway in a battery feature of an electric vehicle).

The processor156may be configured to compare the measured temperature regions302a-306bto the detected objects360a-360d. The region of interest112a-112bof the thermal sensor104may be slightly different than the region of interest (e.g., the field of view)110a-110bof the capture device102. The processor156may be configured to perform disparity operations based on the relative locations and/or viewing angles of the capture device102and the thermal sensor104. In an example, the processor156may be configured to perform the disparity operations based on the distance between the capture device102and the thermal sensor104. The disparity operations may enable the processor156to accurately align the thermal image300with the video frame350. Accurately aligning the thermal image300with the video frame350may ensure that the temperature measurement regions302-306bfrom the thermal image300correspond to the same location in the video frame350. With an accurate alignment of the thermal image300and the video frame350, the sensor fusion module202may be configured to determine the temperature measurement for each of the features402a-402d.

In some embodiments, the processor156may perform a broad temperature analysis. For example, the broad temperature analysis may determine whether any temperature measurement for a particular object is greater than the normal temperature range. In the example shown, the temperature measurement408dmay be 70 C and the normal operating temperature range for the electric vehicle model A may be −20 C to 60 C. Since a temperature has been measured that is greater than the normal temperature range212, the processor156may generate the signal ANOM (e.g., to communicate an early warning). With the broad temperature analysis, any of the temperature measurements408a-408dgreater than the normal temperature range (or increasing faster than the normal temperature change rate) may be detected as a temperature anomaly and the processor156may generate the signal ANOM.

In some embodiments, the processor156may perform a fine-grained temperature analysis. For example, the fine-grained temperature analysis may determine whether the temperature measurement for the particular feature in the anomaly location data214is greater than the normal temperature range. In the example shown, only the temperature measurement408dmay correspond to the temperature anomaly location214(e.g., the battery location for the electric vehicle). Since the temperature measurement408dfor the feature402dcorresponding to the battery location may be 70 C and greater than the normal temperature range212, the processor156may generate the signal ANOM (e.g., to communicate an early warning). With the fine-grained temperature analysis, only particular temperature measurements may be relevant data. For example, if the temperature measurement408acorresponding to the roof feature402aof the vehicle60awas measured to be 70 C (e.g., greater than the normal temperature range) and the battery feature402dof the vehicle60awas measured to be 30 C (e.g., within the normal temperature range), then the processor156may not generate the signal ANOM.

The low resolution thermal image300alone may not provide sufficient information to determine whether the measured temperature corresponds to an object that may be a potential hazard. The high resolution RGB data alone from the video frame350may not provide the temperature measurements to determine whether the detected objects may be a potential hazard. The combination of the data from the thermal image from the thermal sensor104, the data from the computer vision operations performed on the video frames VFRAMES generated from the capture device102and/or the data about the normal temperature ranges for particular classes of objects may be analyzed by the sensor fusion module202to determine whether a temperature anomaly has been detected. The processor156may generate the signal ANOM to enable an early warning communication in response to detecting the temperature anomaly.

The analysis of the combination of thermal imaging and visual imaging may be used to detect hazardous situations. The early warning may be generated in response to the decision module204determining that a scenario may be potentially hazardous in response to the inferences made by the sensor fusion module202. In one example, the hazardous situation may be a detection of thermal runaway of a battery (e.g., a Li-ion battery pack). In another example, the hazardous situation may be a detection of a consistent high temperature inside of a vehicle (e.g., potential harm to a person or pet locked inside of a vehicle). The type of potentially hazardous situation detected in response to determining the temperature anomaly may be varied according to the design criteria of a particular implementation.

Referring toFIG. 8, a method (or process)500is shown. The method500may combine visual and thermal sensing for object-aware temperature anomalies monitoring and early warning. The method500generally comprises a step (or state)502, a step (or state)504, a step (or state)506, a step (or state)508, a step (or state)510, a step (or state)512, a decision step (or state)514, and a step (or state)516.

The step502may start the method500. In the step504, the processor156may receive the pixel data and the thermal image. One or more of the capture devices102a-102nmay present the signals PIXELD_A-PIXELD_N to the processor156. The thermal sensor104may present the signal THIMG to the processor156. Next, in the step506, the processor156may generate video frames from the pixel data. For example, one or more of the dedicated hardware modules190a-190nimplementing the video processing pipeline200may generate video frames from the signals PIXELD_A-PIXELD_N. Next, the method500may move to the step508.

In the step508, the processor156may perform object detection on the video frames VFRAMES. In an example, the video processing pipeline200may present the video frames VFRAMES to the CNN module190bas the video frames are generated to enable real-time object detection. The object detection, along with classification and/or segmentation may be part of the computer vision operations performed by the processor156. Next, in the step510, the processor156may classify the detected objects based on the characteristics detected. The classification may be performed based on the data stored in the object classifications210. In the step512, the processor156may compare the temperature measurement(s) from the thermal image data (e.g., from the signal THIMG) to the normal range of temperatures (e.g., the operating temperatures212) corresponding to the particular classification for the objects detected. Next, the method500may move to the decision step514.

In the decision step514, the processor156may determine whether a temperature anomaly has been detected. The temperature anomaly may be determined based on the measured temperature detected for the particular class of object. In some embodiments, the temperature anomaly may be determined based on the particular location of the measured temperature on the object (e.g., using the anomaly location data214). If a temperature anomaly has not been detected, then the method500may return to the step504. If a temperature anomaly has been detected, then the method500may move to the step516. In the step516, the processor156may generate the control signal ANOM. The control signal ANOM may be used by the communication device154to generate the signal ALERT. The signal ALERT may provide an early warning. In an example, the early warning may provide a notification of a potential fire. Next, the method500may return to the step504.

Referring toFIG. 9, a method (or process)550is shown. The method550may detect a temperature anomaly. The method550generally comprises a step (or state)552, a step (or state)554, a step (or state)556, a step (or state)558, a decision step (or state)560, a decision step (or state)562, a step (or state)564, a step (or state)566, a decision step (or state)568, and a step (or state)570.

The step552may start the method550. In the step554, the CNN module190bmay classify the objects in the visual image (e.g., the video frames generated by the video processing pipeline200from the captured pixel data). Next, in the step556, the sensor fusion module202determine the normal operating temperatures for the objects classified by the CNN module190b. For example, the sensor fusion module202may receive the signal DATA from the memory comprising the operating temperatures data212. In the step558, the sensor fusion module202may analyze the thermal image to detect regions of temperature anomalies. Next, the method550may move to the decision step560.

In the decision step560, the sensor fusion module202may determine whether there is a rapid increase in temperature. In an example, the operating temperatures data212may comprise data about rates of change for temperatures for various types of objects. If there is a rapid increase in temperature, then the method550may move to the step564. If there is not a rapid increase in temperature, then the method550may move to the decision step562.

In the decision step562, the sensor fusion module202may determine whether the detected temperature is above a normal operating range. In an example, the operating temperatures data212may comprise ranges of temperature considered to be a normal operating range for various types of objects. If the detected temperature is not above (e.g., is within) the normal operating range for the particular class of object, then the method550may return to the step554. If the detected temperature is above the normal operating range for the particular class of object, then the method550may move to the step564.

In the step564, the processor156may determine that a temperature anomaly has been detected. Next, in the step566, the sensor fusion module202may localize the region of the temperature anomaly in the visual image. In an example, the sensor fusion module202may be configured to combine and/or compare data from the visual image and the thermal image to determine where the temperature anomaly is located with respect to the object detected. Next, the method550may move to the decision step568.

In the decision step568, the decision module204may determine whether the temperature anomaly corresponds to one of the features402a-402dof the object. In an example, the anomaly location data214may comprise information about areas of concern for temperature anomalies (e.g., locations of batteries for an electronic vehicle, locations of fuel tanks, locations of flammable liquids, etc.). If the detected location of the temperature anomaly does not correspond with one of the features402a-402dthat corresponds to the anomaly location214of the object, then the method550may return to the step554. For example, if the high temperature detected corresponds to the roof of a vehicle, the high temperature may be the result of sunlight on the roof and may not be a cause for concern (e.g., providing an early warning for a temperature anomaly caused by sunlight on the roof may be a false positive). If the detected location of the temperature anomaly does correspond with a feature of the object, then the method550may move to the step570. In the step570, the processor156may generate the signal ANOM to trigger the early warning. Next, the method550may return to the step554.

Referring toFIG. 10, a method (or process)600is shown. The method600may combine thermal data, image data and temperature data. The method600generally comprises a step (or state)602, a step (or state)604, a step (or state)606, a step (or state)608, a step (or state)610, a step (or state)612, a step (or state)614, a step (or state)616, a step (or state)618, a decision step (or state)620, and a step (or state)622.

The step602may start the method600. Next, the method600may move to the step604, the step606and/or the step608. The step604may be one step of a sub-process (comprising the step604and the steps610-614) for processing (e.g., performing computer vision operations on) the video frames. The step606may be one step of a sub-process (comprising the step606and the step616) for processing the thermal image data. The step608may be one step for a sub-process for determining characteristics about the detected objects. The sub-processes may be performed substantially and/or partially in parallel. The sub-processes may each be a component of a pipeline where some steps of one sub-process rely on information determined by another one of the sub-processes in the pipeline.

In the step604, the video processing pipeline200may receive the image data (e.g., one or more of the pixel data PIXELD_A-PIXELD_N). Next, in the step610, the video processing pipeline200may generate the video frames VFRAMES. Next, in the step612, the CNN module190bmay detect objects in the video frames using the computer vision operations. In one example, the classification performed in the step612may determine a general category of the objects detected (e.g., a car, a person, a street sign, etc.). Next, in the step614, the CNN module190bmay perform a fine-grained classification of the object based on the object characteristics. In some embodiments, the step614may be an optional step. In an example, the fine-grained classification may comprise details about the make/model/year of a vehicle. In another example, the fine-grained classification may comprise details about the location that the thermal anomaly is located (e.g., where the car battery is located). In yet another example, the fine-grained classification may identify a particular vehicle (e.g., based on the license plate, by identifying the person entering/exiting the vehicle, based on specific decals or scratches, etc.). Next, the method600may move to the step618.

In the step606, the sensor fusion module202may receive the thermal image data (e.g., the signal THIMG). Next, in the step616, the sensor fusion module202may perform segmentation. The segmentation may be configured to determine where in the thermal image data that a high temperature reading is located. Next, the method600may move to the step618.

In the step608, the sensor fusion module202may receive the normal temperature range for the object detected. In some embodiments, the step608may be performed after, or in parallel to the steps612and/or614to enable the operating temperatures data212to retrieve the normal operating temperatures for the particular class and/or particular feature of the object detected by the CNN module190b. Next, the method600may move to the step618.

In the step618, the sensor fusion module202may combine data from the computer vision analysis with the data from the thermal image analysis. In some embodiments, the data from the segmentation may be used in the step612and/or the step614to correlate the location of the thermal anomalies with the fine-grained classification of the object characteristics. Next, the method600may move to the decision step620.

In the decision step620, the decision module204may determine whether a temperature anomaly has been detected. The detection of the temperature anomaly may be determined in response to determining whether the detected temperatures for particular objects are outside a normal operating temperature range for the class of object detected. If no temperature anomaly has been detected, then the method600may return to the step602. If a temperature anomaly has been detected, then the method600may move to the step622. In the step622, the decision module204may generate the control signal ANOM for the early warning. Next, the method600may return to the step602.

Referring toFIG. 11, a method (or process)650is shown. The method650may localize regions with a temperature anomaly. The method650generally comprises a step (or state)652, a step (or state)654, a decision step (or state)656, a step (or state)658, a step (or state)660, a step (or state)662, a decision step (or state)664, and a step (or state)666.

The step652may start the method650. In the step654, the processor156may analyze the thermal image300generated by the thermal sensor104. Next, the method650may move to the decision step656.

In the decision step656, the processor156may determine whether a temperature anomaly has been detected. In an example, the temperature anomaly may be determined without first determining the class of the objects. For example, the temperature anomaly may be determined by general parameters (e.g., an increase of ten degrees Celsius within thirty seconds, a temperature above sixty degrees Celsius, etc.). If no temperature anomaly has been detected, then the method650may return to the step654. If a temperature anomaly has been detected, then the method650may move to the step658.

In the step658, the processor156may localize the regions of the thermal image300that have the temperature anomaly in the visual image (e.g., the video frame350). For example, the comparison image400may be generated to compare the locations of the temperature anomalies to the visual data. Next, in the step660, the processor160may determine the characteristics of the detected object in the region with the temperature anomaly. For example, the CNN module190bmay analyze the region of the video frame that corresponds to the location of the temperature anomaly (e.g., the detected features402a-402n). In the step662, the sensor fusion module202may combine the information extracted by the processor156from the thermal image300, with the results extracted by the processor156from the computer vision operations. Next, the method650may move to the decision step664.

In the decision step664, the decision module204may determine whether the temperature anomaly is outside the normal operating temperature range for the particular class of object and/or for the particular feature of the object. For example, the processor156may first search the thermal image300for potential anomalies (e.g., in the decision step656), and then search particular regions for specific anomalies (e.g., based on the object class and/or features of the object in the decision step664using computer vision). If the temperature anomaly is within the normal operating range for the object, then the method650may return to the step654. If the temperature anomaly is outside of the normal operating temperature range for the object, then the method650may move to the step666. In the step666, the processor156may generate the signal ANOM. The signal ANOM may be used to initiate the early warning alert. Next, the method650may return to the step654. For example, the monitoring may be continuous.