# US 12,008,814 B2 · Adjusting areas of interest for motion detection in camera scenes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,008,814 B2 - Document ID: US12008814B2 - Status: Granted - Kind: B2 - Application number: 17/739,237 - Filed: 2022-05-09 - Published: 2022-11-17 - Granted: 2024-06-11 - Earliest claimed: 2021-05-11 - Assignees: ObjectVideo Labs, LLC - Applicants: ObjectVideo Labs, LLC - Inventors: Narayanan Ramanathan; Gang Qian; Eduardo Romera Carmena; Donald Gerard Madden; Allison Beach - Web page: https://patentgrep.com/patent/US12008814B2 ## CPC - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - H04N 7/183: {for receiving images from a single remote source} - G06T 7/11: Region-based segmentation - G06T 7/174: involving the use of two or more images - G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) - G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 20/70: Labelling scene content, e.g. deriving syntactic or semantic representations - G08B 13/19602: {Image analysis to detect motion of the intruder, e.g. by frame subtraction} - G08B 13/19695: {Arrangements wherein non-video detectors start video recording or forwarding but do not generate an alarm themselves} - H04N 7/188: {Capturing isolated or intermittent images triggered by the occurrence of a predetermined event, e.g. an object reaching a predetermined position (signal generation from motion picture films H04N5/253)} - G06T 2207/10016: Video; Image sequence - G06T 2207/20081: Training; Learning - G06T 2207/20104: Interactive definition of region of interest [ROI] - G06T 2207/30232: Surveillance - G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS ## Abstract Disclosed are methods, systems, and apparatus for adjusting areas of interest for motion detection in camera scenes. A method includes obtaining a map of false motion event detections using a first area of interest; identifying an overlap area between the map of false detections and the first area of interest; determining a second area of interest that includes portions of the first area of interest and excludes at least a part of the overlap area; obtaining a map of true motion event detections using the first area of interest; determining whether true detections using the second area of interest compared to true detections using the first area of interest satisfies performance criteria; and in response to determining that true detections using the second area of interest compared to true detections using the first area of interest satisfies performance criteria, providing the second area of interest for use in detecting events. ## Claims ### Claim 1 A method comprising: providing, for use in detecting events depicted in one or more first images, a first area of interest of a scene within a field of view of a camera; obtaining a map of false motion events that were detected using the first area of interest; in response to obtaining the map of false motion events, identifying an overlap area between the map of false motion events and the first area of interest; determining a second area of interest that includes one or more portions of the first area of interest and excludes at least a part of the overlap area between the map of false motion events and the first area of interest; obtaining a map of true motion events that were detected using the first area of interest; determining, using the map of true motion events, a performance metric for using the first area of interest to detect events; determining, using the map of true motion events, a predicted performance metric for using the second area of interest to detect events; determining whether the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy performance criteria; and in response to determining that the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy the performance criteria, providing the second area of interest for use in detecting events depicted in one or more different, second images instead of the first area of interest. ### Claim 2 (depends on 1) The method of claim 1, comprising generating the map of false motion events and the map of true motion events by: obtaining data defining the first area of interest; obtaining the one or more first images; detecting a plurality of motion events in the one or more first images, each motion event representing motion of an object within the field of view of the camera; and classifying each motion event as a false motion event or a true motion event. ### Claim 3 (depends on 2) The method of claim 2, comprising: determining a traversal score for each motion event, wherein the traversal score indicates an extent of motion of the object across the field of view of the camera, wherein classifying each motion event as a true motion event or a false motion event uses the traversal score. ### Claim 4 (depends on 3) The method of claim 3, wherein determining a traversal score comprises, for each motion event representing motion of an object: determining a centroid location of the object in each frame of a frame set, the frame set including multiple sequential image frames in which the object was detected during the motion event; determining a traversal metric using a distance between the centroid location in a first frame of the frame set and the centroid location in a final frame of the frame set; and determining, for each motion event, a corresponding traversal score by combining traversal metrics for a plurality of frame sets, each frame set including multiple sequential image frames in which the object was detected during the motion event. ### Claim 5 (depends on 1) The method of claim 1, wherein the map of false motion events includes an outline encompassing pixels that correspond to locations of the scene where false motion events occurred. ### Claim 6 (depends on 1) The method of claim 1, wherein the map of true motion events includes an outline encompassing pixels that correspond to locations of the scene where true motion events occurred, the method comprising generating the map of true motion events by: generating a plurality of bounding boxes, wherein each bounding box includes at least an upper boundary and a lower boundary and encloses a location of the scene where a true motion event occurred; and generating the outline, wherein the outline encompasses a portion of each bounding box that includes the lower boundary of the bounding box. ### Claim 7 (depends on 1) The method of claim 1, wherein determining the second area of interest comprises: labeling segments of the scene according to objects represented by the segments; generating a copy of the first area of interest; and adjusting the copy of the first area of interest based on the labels of the segments of the scene to obtain the second area of interest. ### Claim 8 (depends on 7) The method of claim 7, wherein adjusting the copy of the first area of interest based on the labels of the segments of the scene comprises: determining a label for a particular segment of the scene within the overlap area between the map of false motion events and the first area of interest; classifying the label of the particular segment as corresponding to an object that is not of interest; and based on classifying the label of the particular segment as corresponding to an object that is not of interest, removing the particular segment from the copy of the first area of interest. ### Claim 9 (depends on 7) The method of claim 7, wherein adjusting the copy of the first area of interest using the labels of the segments of the scene comprises: determining a label for a particular segment of the scene within the overlap area between the map of false motion events and the first area of interest; classifying the label of the particular segment as corresponding to an object that is of interest; and based on classifying the label of the particular segment as corresponding to an object that is of interest, maintaining the particular segment within the first area of interest. ### Claim 10 (depends on 1) The method of claim 1, wherein determining whether the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy performance criteria comprises determining an impact of using the second area of interest on at least one of: a recall of the camera; a detection latency of the camera; or a degree of overlap between the respective area of interest and the map of true motion events. ### Claim 11 (depends on 1) The method of claim 1, wherein the performance criteria comprise a maximum threshold impact on a recall of the camera, the recall of the camera using a ratio of a number of true motion events to a total number of true objects of interest depicted in images captured by the camera during a duration of time. ### Claim 12 (depends on 1) The method of claim 1, wherein the performance criteria comprise a maximum threshold impact on a true motion overlap score of the camera, the true motion overlap score using a degree of overlap between a map of true motion events and a respective area of interest. ### Claim 13 (depends on 1) The method of claim 1, wherein the performance criteria comprise a maximum threshold impact on detection latency of the camera, the detection latency using a time delay between a time when an event occurs and a time when the camera detects the event. ### Claim 14 (depends on 1) The method of claim 1 comprising: detecting a decrease in performance of the camera while detecting events using the second area of interest; and in response to detecting the decrease in performance of the camera, providing the first area of interest for use in detecting events. ### Claim 15 (depends on 14) The method of claim 14, wherein detecting the decrease in performance comprises determining that an average rate of false motion events that occur while detecting events using the second area of interest is greater than an average rate of false motion events that occur while detecting events using the first area of interest. ### Claim 16 A system comprising one or more computers and one or more computer storage media storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: providing, for use in detecting events depicted in one or more first images, a first area of interest of a camera; obtaining a map of false motion events that were detected using the first area of interest; in response to obtaining the map of false motion events, identifying an overlap area between the map of false motion events and the first area of interest; determining a second area of interest that includes one or more portions of the first area of interest and excludes at least a part of the overlap area between the map of false motion events and the first area of interest; obtaining a map of true motion events that were detected using the first area of interest; determining, using the map of true motion events, a performance metric for using the first area of interest to detect events; determining, using the map of true motion events, a predicted performance metric for using the second area of interest to detect events; determining whether the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy performance criteria; and in response to determining that the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy the performance criteria, providing the second area of interest for use in detecting events depicted in one or more different, second images instead of the first area of interest. ### Claim 17 A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: providing, for use in detecting events depicting in one or more first images, a first area of interest of a camera; obtaining a map of false motion events that were detected using the first area of interest; in response to obtaining the map of false motion events, identifying an overlap area between the map of false motion events and the first area of interest; determining a second area of interest that includes one or more portions of the first area of interest and excludes at least a part of the overlap area between the map of false motion events and the first area of interest; obtaining a map of true motion events that were detected using the first area of interest; determining, using the map of true motion events, a performance metric for using the first area of interest to detect events; determining, using the map of true motion events, a predicted performance metric for using the second area of interest to detect events; determining whether the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy performance criteria; and in response to determining that the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy the performance criteria, providing the second area of interest for use in detecting events depicted in one or more different, second images instead of the first area of interest. ### Claim 18 (depends on 1) The method of claim 1, wherein determining whether the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy performance criteria comprises: determining that the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy the performance criteria. ### Claim 19 (depends on 1) The method of claim 1, wherein determining whether the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfy performance criteria comprises: determining that a difference between the performance metric for using the first area of interest to detect events and the predicted performance metric for using the second area of interest to detect events satisfies the performance criteria. ### Claim 20 (depends on 1) The method of claim 1, wherein the overlap area between the map of false motion events and the first area of interest comprises a plurality of pixels in common between the map of false motion events and the first area of interest.