Avigilon Fortress Corporation

Objectvideo, Inc

Shanghai Truthvision Information Technology Co., Ltd.

Alarm.com Incorporated

Janssen Biotech, Inc.

Transform Pharmaceuticals, Inc.

Motorola Solutions, Inc.

Diamondback Vision, Inc.

Centocor Ortho Biotech Inc.

Zhong Zhang

Peter L Venetianer

Weihong Yin

Alan J Lipton

Niels Haering

Li Yu

Andrew J Chosak

Gary W Myers

Zeeshan Rasheed

Paul C Brewer

Matthew F Frazier

W Andrew Scanlon

Yongtong Hu

Dana Eubanks

John I W Clark

Himaanshu Gupta

Donald Gerard Madden

Péter L Venetianer

Omar Javed

James S Sfekas

Allison Beach

Haiying Liu

Mark Allmen

Andrew J Martone

Orn Almarsson

Geoffrey Egnal

Thomas M Strat

Michael C Mannebach

Hongming Chen

William E Severson

Tasuki Hirata

Kyle Glowacki

Steve Titus

Narayanan Ramanathan

Robert A Cutting

Don Madden

James Morris

Thomas E Slowe

Bruce Thompson

Amit Mistry

Wei Hong Yin

Pèter L Venetianer

John Ian Wallace Clark

Julius F Remenar

Khurram Hassan-Shafique

Mun Wai Lee

Geoffrey R Taylor

Xiao Chun Cao

Anthony J Meehan, Jr

James S Seekas

John Clark

Liangyin Yu

Caspar Horne

Kelly Schomburg

John F Tilki

Anthony J Meehan

Mark C Allmen

A method may include obtaining a video collected by a visual sensor, the video including a plurality of frames and detecting one or more objects from the video in at least a portion of the plurality of frames. The method may also include determining a first detection result associated with the one or more objects with a trained self-learning model. The method may further include selecting a target moving object of interest from the one or more objects at least in part based on the first detection result. The trained self-learning model may be provided based on a plurality of training samples collected by the visual sensor.

Systems and methods for intelligent video surveillance

A method for detecting abnormal scene may include obtaining data relating to a video scene, identify at least two motion objects in the video scene based on the data and determining a first motion feature relating to the at least two motion objects based on the data. The method may also include determining a second motion feature relating to at least one portion of each of the at least two motion objects based on the data. The method may further include determining whether the at least two motion objects are involved in a fight based on the first motion feature and the second motion feature.

System and method for abnormal scene detection

A system for motion detection may include at least one storage medium that includes a set of instructions, and at least one processor in communication with the at least one storage medium. When executing the set of instructions, the at least one processor may be configured to cause the system to obtain data related to a video scene of a space from at least one video camera; detect an object in the video scene; classify the object as a human object or a non-human object; when the object is classified as a human object, track movements of the human object; and determine a posture of the human object in the video scene based on the movements of the human object.

Systems and methods for detecting a posture of a human object

A method and system using face tracking and object tracking is disclosed. The method and system use face tracking, location, and/or recognition to enhance object tracking, and use object tracking and/or location to enhance face tracking.

Object tracking and best shot detection system

A method for traffic surveillance may include acquiring, from a plurality of first video sensors, a first set of images of an area, and generating a map-based surveillance interface of the area based on the first set of image data. The method may also include presenting, on a display, the map-based surveillance interface; wherein each of the plurality of first video sensors is implemented on an illuminating devices in the area, and the first set of image data denotes a plurality of views of the area captured by the plurality of first video sensors, respectively.

System and method for traffic surveillance

A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network may include: obtaining a head image of a user with one or more dimension indicators, determining an eye region in the head image of the user and determining a pupillary distance of the user based on the one or more dimension indicators and the determined eye region. A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network may include: receiving a request at a user terminal, recording a video of a user wearing a wearable device with the user terminal, determining a pupillary distance of the user based on the video and synchronously displaying the pupillary distance on the video of the user during recording. The wearable device may include one or more dimension indicators.

System and method for measuring pupillary distance

A system and method for determining a traffic sign violation are provided. The method may include obtaining a traffic rule corresponding to the traffic sign. The method may further include acquiring, by at least one camera, video data associated with a scene around a traffic sign. The video data may include a series of frames. The method may further include identifying the vehicle in the series of frames and determining whether the vehicle violates the traffic rule based on the series of frames. In response to the determination that the vehicle violates the traffic rule, the method may further include obtaining information of the vehicle and transmitting the information of the vehicle to a server.

Systems and methods for monitoring traffic sign violation

A method may include obtaining real-time image data relating to at least one scene acquired by a camera and identifying a plurality of real-time first features relating to a plurality of subjects from the real-time image data. The method may include determining one or more first estimated values corresponding to the plurality of real-time first features, the first estimated values being represented by a first coordinate system. The method may further include obtaining one or more first reference values corresponding to the plurality of real-time first features, the first reference values being represented by a second coordinate system, each of the first estimated values corresponding to one of the first reference values. The method may further include determining one or more real-time target parameters of the camera based on the first estimated values and the first reference values.

System and method for camera calibration

A method for smart door operation implemented on a computer may include obtaining image data of one or more subjects. The image data may be acquired by one or more cameras in communication with the computer. The method may also include determining, based on the image data, one or more gait features of at least one of the one or more subjects and determining, at least in part based on the one or more gait features, identity information of the at least one of the one or more subjects. The method may further include sending an unlocking signal to a lock based on the unlocking signal so that the lock is unlocked.

Smart door system

The system for lighting control may include a non-transitory storage medium storing executable instructions for lighting control, a communication component operatively connected to the non-transitory storage medium, at least one imaging device configured to capture images related to an area, and one or more illuminating device configured to light the area. The system may also include at least one processor in communication with the non-transitory storage medium. When executing the set of instructions, the at least one processor may cause the system to obtain image data relating to an area captured by the at least imaging device and determine at least one parameter relating to at least one of the one or more illuminating devices. The system may further include a control component configured to operate the at least one of the one or more illuminating devices to light the area based on the determined at least one parameter.

System and method for lighting control

Methods and systems are provided for monitoring a point of sale (POS) transaction. Operations performed by the methods and systems include generating POS primitives by processing non-video data of a transaction recorded at POS terminal. The operations also include generating video primitives by processing video data of the transaction recorded at the POS terminal. The operations further include determining that the transaction comprises an exceptional transaction by comparing the non-video data and/or the video data to exceptional transaction rules. Additionally, the operations include determining that the exceptional transaction comprises a verified exceptional transaction by generating a video event based on the video primitives and a corresponding video rule.

Intelligent video verification of point of sale (POS) transactions

Systems, methods and computer-readable media for creating and using video analysis rules that are based on map data are disclosed. A sensor(s), such as a video camera, can track and monitor a geographic location, such as a road, pipeline, or other location or installation. A video analytics engine can receive video streams from the sensor, and identify a location of the imaged view in a geo-registered map space, such as a latitude-longitude defined map space. A user can operate a graphical user interface to draw, enter, select, and/or otherwise input on a map a set of rules for detection of events in the monitored scene, such as tripwires and areas of interest. When tripwires, areas of interest, and/or other features are approached or crossed, the engine can perform responsive actions, such as generating an alert and sending it to a user.

Systems and methods for video analysis rules based on map data

Embodiments relate to systems, devices, and computer-implemented methods for performing automated visual surveillance by obtaining video camera coordinates determined using video data, video camera metadata, and/or digital elevation models, obtaining a surveillance rule associated with rule coordinates, identifying a video camera that is associated with video camera coordinates that include at least part of the rule coordinates, and transmitting the surveillance rule to a computing device associated with the video camera. The rule coordinates can be automatically determined based on received coordinates of an object. Additionally, the surveillance rule can be generated based on instructions from a user in a natural language syntax.

Systems and methods for automated visual surveillance

A video surveillance system is set up, calibrated, tasked, and operated. The system extracts video primitives and extracts even occurrences from the video primitives using event discriminators. The extracted video primitives and event occurrences may be used to create and define additional video analytic rules. The system can undertake a response, such as an alarm, based on extracted event occurrences.

Video analytic rule detection system and method

Growing community of inventors

Great Falls, VA, United States of America

Zhong Zhang