Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Aspects of the present disclosure provide techniques that utilize artificial intelligence (AI) to preemptively identify and prioritize risks for retailers from shrink loss. Specifically, features of the present disclosure provide actionable prescriptions to prevent shrink based on the output of a hybrid machine learning (ML) system that accurately identifies shrink risk factors and implements a cost-effective shrinkage control plan to reduce the retail theft. The shrinkage control plan may then be displayed to the user on a display device (or user interface) in order to allow for the user (e.g., store manager) to implement the recommendations developed by the recommendation engine.

Abstract: Example aspects include methods, apparatuses, and computer-readable medium for receiving, by an antenna of a tag tracking/detection system, a tag signal of a tag in the tag tracking/detection system; determining, based on a strength or pattern of the tag signal, whether the tag signal comprises a reflected signal that is reflected by one or more reflective surfaces, wherein the one or more reflective surfaces are configured to focus tag signals of tags in a target location of the tag tracking/detection system toward the antenna of the tag tracking/detection system; and generating an output signal responsive to the tag signal comprising the reflected signal that is reflected by the one or more reflective surfaces, wherein the output signal is indicative of the tag being detected in the target location.

Abstract: A system may be configured to perform enhanced detection of occluded objects in a multiple object detection system. In some aspects, the system may determine a first size of a queue captured in a video frame based on an object detection system and a video capture device, and determine a second size of the queue based on the first size of the queue and a correction model. Further, the system may detect a person at a front of the queue based on an object tracking system, determine a first period of time spent by the person at the front of the queue based on an object tracking system, and estimate a second period of time spent in the queue based at least in part on the first period of time and the second size of the queue.

Abstract: Disclosed herein are apparatuses and methods for object detection in an environment. An implementation may comprise detecting, using at least one sensor, persons that entered and exited the environment during a first period of time and determining an entry count and an exit count accordingly. The implementation may comprise retrieving, from a database, historical ingress and egress data comprising entry and exit counts of the environment for a second period of time. The implementation may comprise determining an amount of expected exit counts for the first period of time based on the historical ingress and egress data of the second period of time by fitting the historical ingress and egress data to a first probability distribution. The implementation may comprise updating the exit count for the first period of time to the amount of expected exit counts and storing the updated exit count in the database.

Abstract: Example aspects include techniques for building a ML model in a use case with prohibitive training data and employing the ML model within the use case. These techniques may include determining training information including a plurality of stray training reads and a plurality of valid training reads, determining modified training information based at least in part on modifying the plurality of valid training reads, and generating a model for distinguishing a valid read from a stray read based on the modified training information and an evolutionary algorithm. In addition, the techniques may include detecting, by a monitoring device, a plurality of tag reads in response to a plurality of interactions between a tag and the monitoring device, and determining, by the monitoring device, a plurality of valid tag reads based on the model and plurality of tag reads.

Abstract: A system may be configured to implement a probability network for loss prevention sensors. In some aspects, the system may determine a location transition of a tag associated with an article, determine a first predictive value based at least in part on a first predictive structure and the location transition, the first predictive structure corresponding to a valid detection alarm, and determine a second predictive value based at least in part on a second predictive structure and the location transition, the second predictive structure corresponding to an invalid detection alarm. Further, the system may generate a notification based at least in on part on comparing the first predictive value to the second predictive value, the notification indicating unauthorized movement of the tag outside of a geographic area.

Abstract: A system may be configured to reliably identify, track, and collect data on a person of interest. In some aspects, the system may detect a facial representation within an image, generate a bounding box corresponding to the facial representation, determine an enhanced facial representation based at least in part on the bounding box and an image enhancement pipeline, and extract a plurality of facial features from the enhanced facial representation. Further, the system may determine quality information based on the plurality of facial features, determine that the quality information is greater than a threshold, and store the plurality of facial features in a data structure. In some aspects, the facial features may be used to identify red shoppers and green shoppers in a controlled environment.

Abstract: 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.

Abstract: A system may be configured to accurately track shelf activity in real-time with support for dynamic shelf size, configuration, and item containment. In some aspects, the system may parse regions of a video frame to determine a region of interest representation corresponding to a physical location (e.g., a shelf compartment), determine an enhanced region of interest representation based at least in part on the region of interest representation and an image enhancement pipeline, determine edge information of one or more objects based on the enhanced region of interest representation, compare a reference representation of the physical location to the edge information, and determine the amount of available space for the physical location based on the comparing.

Abstract: 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.

Abstract: 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.

Abstract: Example aspects include methods, apparatuses, and computer-readable medium for receiving, by an antenna of a tag tracking/detection system, a tag signal of a tag in the tag tracking/detection system; determining, based on a strength or pattern of the tag signal, whether the tag signal comprises a reflected signal that is reflected by one or more reflective surfaces, wherein the one or more reflective surfaces are configured to focus tag signals of tags in a target location of the tag tracking/detection system toward the antenna of the tag tracking/detection system; and generating an output signal responsive to the tag signal comprising the reflected signal that is reflected by the one or more reflective surfaces, wherein the output signal is indicative of the tag being detected in the target location.

Abstract: An epicardial clip for reshaping the annulus of the mitral valve of a heart includes a curved member having an anterior segment configured to be positioned in the transverse sinus of the heart, a posterior segment configured to be positioned on the posterior side of the heart, such as on or inferior to the atrioventricular groove, and a lateral segment extending between the anterior segment and the posterior segment. The lateral segment includes a curve such that the first end of the member is positioned at or above the plane of the mitral valve and the second end of the member is positioned at or below the plane of the mitral valve.

Abstract: Aspects of the present disclosure provide techniques to implement artificial intelligence (AI) that preemptively identifies and prioritizes risks for retailers for shrink loss. Specifically, features of the present disclosure provide a hybrid machine learning (ML) techniques that selects two or more machine learning algorithms to develop a machine learning model to accurately identify shrink risk factors and implement a cost-effective shrinkage control plan to control the retail theft.

Abstract: Systems and methods for alarm panel analysis include receiving a plurality of alarm events from the respective alarm panels monitoring corresponding buildings. An alarm analysis system may classify the alarm panels by identifying an alarm type for the alarm events. The alarm analysis system may determine a number of occurrences of each alarm type for the alarm panels, and generate a data point for a dataset for the alarm panels. The alarm analysis system may identify statistical dividers in the data points for the data set, which may be used for assigning a ranking for the alarm panels based on their location in relation to the statistical dividers. The alarm analysis system may construct and a monitoring dashboard which includes the rankings of the alarm panels, which may be rendered on a display to an end user.