Sensormatic Electronics Corporation

Johnson Controls Technology Company

General Electric Company

Tyco Fire + Security Gmbh

Johnson Controls Tyco IP Holdings Llp

Gopi Subramanian

Joseph Celi, Jr

Michael C Stewart

Christine Trainor

Jason Kapuschat

Michael Stewart

Abdul Razak

James Trani

Conor Joseph Donovan

Cory Olog Root

Adam Scott Bergman

Paul Edward Cuddihy

David Vavrasek

Giridhar M Prabhakar

Michelangelo Palella

Jeffrey R Rendlen

Debasis Bal

Babu Ozhur Narayanan

Ramasubramanian Gangaikondan Sundararajan

Thomas Giles Szudajski

Harish Yadav

Milton R Navarro

Karthik Jayaraman

Rajavenkateswaran Rajamanickam

William Dubois

A system may be configured to generate real-time analytics using machine learning and computer vision. In some aspects, the system may receive a storage structure video frame and an interaction video frame, determine a storage structure inference based on the storage structure video frame, and determine an interaction inference based on the interaction video frame. Further, the system may determine that the storage structure inference and the interaction inference correspond to a common time period and common location, and generate analytics information based on the storage structure inference and the interaction inference.

Method and system to provide real time interior analytics using machine learning and computer vision

The present disclosure relates to a system for determining anomalous behavior. The system comprises a processing device that is configured to generate first segmentation information and second segmentation information from the video feed, the first segmentation information corresponding to a first potential occupant and the second segmentation information corresponding to a second potential occupant. The processing device further configured to determine that the first segmentation information and the second segmentation information correspond to actual occupants based on the first segmentation information and the second segmentation information, and generate a first bounding box associated with the first segmentation information and a second bounding box associated with the second segmentation information. The processing device further configured to determine an intersection over union (IOU) region for the first bounding box and the second bounding box, identify anomalous behavior based on the IOU region, and generate one or more alerts identifying the anomalous behavior.

System and method to determine anomalous behavior

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.

Computer vision systems and methods for detecting person dwell time

Systems and methods are disclosed for detecting a security event at an establishment, obtaining, based on detecting the security event, vehicle information associated with a vehicle used in conjunction with the security event, storing the vehicle information, detecting, based on the vehicle information, the vehicle arriving at the establishment or another establishment, and generating, based on detecting the vehicle arriving at the establishment or another establishment, a notification that the vehicle has arrived at the establishment or another establishment.

Systems and methods for repeat offender anti-theft notification based on vehicle identification

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.

Systems and methods for vision system tracking in an environment based on detected movements and dwell time

Example aspects include a method, apparatus and computer-readable medium of determining losses at a point of sale (POS) device, comprising receiving, by a processor from an imaging device, a video feed of a scanning area. The aspects further include detecting, by the processor, an entry of an item into the scanning area. Additionally, the aspects further include identifying, by the processor, one or more motion parameters of the item. Additionally, the aspects further include determining, by the processor, a dwell-time for the item based at least on the one or more motion parameters. Additionally, the aspects further include identifying, by the processor, a scan time anomaly for the item. Additionally, the aspects further include outputting a notification, by the processor, indicating a suspicious activity for the item, wherein the notification indicating the suspicious activity is based on the scan time anomaly.

Scan avoidance prevention system

Disclosed herein are apparatuses and methods for iteratively mapping a layout of an environment. The implementations include receiving a visual stream from a camera installed in the environment, wherein the visual stream depicts a view of the environment, and wherein positional parameters of the camera and dimensions of the environment are set to arbitrary values. The implementations include monitoring a plurality of persons in the visual stream. For each person in the plurality of persons, the implementations further includes identifying a respective path that the person moves along in the view, updating the dimensions of the environment captured in the view, based on an estimated height of the person and movement speed along the respective path, and updating the positional parameters of the camera based on the updated dimensions of the environment. The implementations further includes mapping a layout of the environment captured in the view of the camera.

Layout mapping based on paths taken in an environment

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.

Person-of-interest (POI) detection

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.

Techniques for preventing shrink based on hybrid machine learning system

A system for preventing a false alarm that occurs at a building, the system includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices. The processing circuit is configured to determine, based on the events, whether a false alarm rule has triggered, where the false alarm rule indicates relationships between one or more of the events that is indicative of a situation at the building site that causes the false alarm, generate a parameter update for at least one of the plurality of building devices in response to determining that the false alarm rule has triggered, and implement the parameter update by providing, via the communications interface, the parameter update to the at least one of the building devices.

Building security system with false alarm reduction recommendations and automated self-healing for false alarm reduction

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.

Facilitating ML algorithm contribution to use cases where data is prohibitive

Disclosed herein are apparatuses and methods for detecting a slip, trip or fall event in an environment and sending an alert of the event. An implementation may comprise detecting, in image frames captured by a camera, when an object enters a region of interest, tracking movements of the object, and determining that the object is in a fall-zone. The fall-zone may be specified by a set of line segments, each line segment being defined by points that lie in the region of interest. The implementation may further comprise, when the object is in the fall-zone, recording the position of the object, detecting a slip, trip or fall event when the object transitions from a first position above a predetermined height threshold to a second position below the predetermined height threshold, a pose of the object indicates that the object is lying down, or the object transitions from a vertical to horizontal pose, and sending the alert.

Systems and methods for detecting a slip, trip or fall

A system may be configured to perform enhanced detection of occluded objects in a multiple object detection system. In some aspects, the system may detect a plurality of current objects in a current video frame, generate initial object identifier mapping information assigning a candidate object identifier to a first object of the plurality of current objects, and detect a multi-object tracking (MOT) inaccuracy condition. Further, the system may generate a current fingerprint corresponding to the first object of the plurality of current objects, identify an assignment error based on comparing the current fingerprint to historic fingerprints associated with a plurality of historic objects detected in a previous frame, generate updated object identifier mapping information based on the assignment error, and displaying the current video frame based on the updated object identifier mapping information.

Multi-pass object tracking system utilizing single object tracking in a multi object tracking use case for higher accuracy

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.

Automated alarm panel classification using pareto optimization

Disclosed herein are apparatuses and methods for auto-configuring a region of interest (ROI) associated with a camera. In one implementation, a method comprises receiving image frames from a camera installed in the environment, wherein the ROI is located within the view of the image frames. The method includes tracking a plurality of persons in the image frames and determining a respective trajectory of movement for each person of the plurality of persons. The method further includes comparing each of the respective trajectories to one another and identifying, based on the comparing, a common trajectory shared by more than one person of the plurality of persons, wherein the common trajectory is not fully encompassed in the ROI. The method additionally includes updating the ROI to encompass the common trajectory, and includes configuring the updated ROI to be associated with new image frames from the camera.

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Boca Raton, FL, United States of America

Gopi Subramanian