Sensormatic Electronics, LLC

Disclosed herein are apparatuses and methods for auto-configuring a region of interest (ROI) associated with a camera. In one implementation, A method includes tracking, over a plurality of image frames spanning a first period of time, a plurality of objects moving in an environment, and determining a plurality of respective trajectories of the plurality of objects. The method includes identifying a common trajectory shared by more than one object of the plurality of objects based on comparing each of the plurality of respective trajectories to one another and identifying an ROI that encompasses the common trajectory. The method further includes tracking, over a plurality of new image frames spanning a second period of time, another plurality of objects moving in the environment, and generating an alert that the common trajectory has changed based on new trajectories of the another plurality of objects.

Aspects of the present disclosure relate generally to a vision system that detects objects moving in an environment. The vision system detects objects that crossed a boundary of an environment during a first period of time, and determines a count of objects that crossed the boundary in a first direction. The vision system retrieves historical egress and ingress data for a second period of time corresponding to the first period of time. The historical egress and ingress data comprises a detected historic count of crossings in the first direction and a corrected historic count of crossings in the first direction. The vision system calculates an error rate for the second period of time based on a ratio of the detected historic count and the corrected historic count, and determines a corrected count for the first period of time by adjusting the count using the error rate.

Example implementations include a method, apparatus and computer-readable medium for computer vision detection of dwell time, comprising receiving a plurality of indications of persons entering an environment over a period of time. The implementations further include adding, into a first-in-first-out (FIFO) queue, a respective identifier of a respective person entering the environment and an entry time for each of the plurality of indications, removing, from the FIFO queue, the respective identifier and the entry time of the respective person in response to an indication of an exit of the respective person from the environment at an exit time, calculating an estimated dwell time of the respective person based on a difference in the entry time and the exit time, and generating an alert based on calculating an average dwell time of the persons entering the environment over the period of time based on each estimated dwell time.

Disclosed herein are apparatuses and methods for tracking occupancy count in an environment based on detected movements and dwell time. An implementation may comprise detecting a person in a plurality of image frames captured by a camera. The implementation may comprise tracking a movement of the person and determining that the person has crossed a boundary within the plurality of image frames. The implementation may comprise identifying a path of the person based on the tracked movement and may comprise calculating a dwell time in response to determining that a difference between the identified path and at least one target path associated with entering or exiting the environment exceeds a threshold difference. The implementation may comprise comparing the dwell time to a target dwell time, and updating and storing the occupancy count of the environment in response to determining that the dwell time is less than the target dwell time.

Example implementations include a method, apparatus and computer-readable medium of computer vision configured for person classification, comprising receiving, during a first period of time, a plurality of image frames of an environment, identifying images of persons from each frame of the plurality of image frames, and determining a respective vector representation of each of the images. The implementations include generating a probability distribution indicative of a likelihood of a particular vector representation appearing in the plurality of image frames and identifying an associate vector representation by sampling the probability distribution using a probability model. The implementations include determining an input vector representation of an input image identified in an image frame depicting a person and received during a second period of time. The implementations include comparing the input vector representation with the associate vector representation and classifying, based on the comparison, the person in the input image as an associate.

Example implementations include a method, apparatus and computer-readable medium for person detection by a computer device, including retrieving, from a database of suspicious persons, a profile of a person detected in at least one image, wherein the profile lists crime incidents associated with the person. The implementations include determining, using the profile, a scale of crimes committed by the person based on an amount of time between incidents of a certain type, wherein the certain type is one of theft at a store or theft of a particular product type. The implementations include determining a rank of the person in the database of suspicious persons based on a frequency and the scale of crimes committed by the person compared to other suspicious persons, and transmitting, to a second computer device, the retrieved profile and a notification that indicates that the person is detected when the rank exceeds a threshold rank.

Aspects of the present disclosure relate generally to a vision system that detects objects moving in an environment. The vision system detects objects that crossed a boundary of an environment during a first period of time, and determines a count of objects that crossed the boundary in a first direction. The vision system retrieves historical egress and ingress data for a second period of time corresponding to the first period of time. The historical egress and ingress data comprises a detected historic count of crossings in the first direction and a corrected historic count of crossings in the first direction. The vision system calculates an error rate for the second period of time based on a ratio of the detected historic count and the corrected historic count, and determines a corrected count for the first period of time by adjusting the count using the error rate.

Aspects of the present disclosure include methods, systems, and non-transitory computer readable media for receiving an initial image of the area, receiving an updated image of the area, detecting at least one of a person in the area or a movement in the area based on the updated image, comparing the updated image and the initial image to identify a change in the area, and determining a presence or an absence of the occlusion based on the change in the area and the at least one of the person or the movement in the area.

Example implementations include a method, apparatus and computer-readable medium for person detection by a computer device, comprising receiving a plurality of video frames from a camera located in an environment, wherein the video frames include a depiction of a person in the environment. The implementations further include identifying attributes of the person from the video frames and comparing the attributes with attribute entries in a database of suspicious persons, wherein the database of suspicious persons includes attributes of a plurality of persons associated with an alert in the environment. The implementations include retrieving a profile indicative of an alert history of the suspicious person in response to determining a match between the attributes of the person and an entry in the database corresponding to a suspicious person, and transmitting, to a second computer device, the retrieved profile and a notification that indicates that the suspicious person is in the environment.

Aspects of the present disclosure relate generally to a vision system that detects persons exiting an environment, comprising detecting, by a processor using a plurality of image frames from at least one sensor, persons that exited the environment during a first period of time. The vision system further determines, by the processor, an exit count for the first period of time and retrieves, from a database, historical egress data comprising a detected historic exit count and a corrected historic exit count of the environment for a second period of time corresponding to the first period of time. The vision system calculates an error rate for the second period of time based on a ratio of the detected historic exit count and the corrected historic exit count, determines and stores a corrected exit count for the first period of time by adjusting the exit count using the error rate.