Avigilon Fortress Corporation

Objectvideo, Inc

Samsung Electronics Co., Ltd.

Remoteretail, Inc.

Snap+style, Inc

Gary W Myers

Alan J Lipton

Zhong Zhang

Andrew J Chosak

Peter L Venetianer

Weihong Yin

Niels Haering

John I W Clark

Robert A Cutting

Matthew F Frazier

W Andrew Scanlon

Donald Gerard Madden

Péter L Venetianer

James S Sfekas

Michael C Mannebach

Geoffrey Egnal

Omar Javed

Zeeshan Rasheed

Paul C Brewer

Khanh Lam

John Ian Wallace Clark

Anna Jensen

Raul Fernandez

William McComb

Albert Au

Chung-Cheng Yen

Li Yu

Yongtong Hu

Andrew J Martone

Ethan Shayne

David Conger

James Morris

Pramod Kalapa

Nathan D Borrebach

Satyajeet S Hoskote

William Aldrich

John Iw Clark

A system and method to automatically generate a secondary video stream based on an incoming primary video stream. The method including performing video analytics on the primary video stream to generate one or more analysis results, detecting the first target of interest using the analysis results, automatically extracting a first secondary video stream that captures at least a portion of a first target of interest and has a field of view smaller than that of the primary video stream, tracking the first target of interest, displaying the first secondary video stream, detecting a second target of interest using the analysis results, automatically adapting the first secondary video stream from the primary video stream to capture a portion of the first and second targets of interest, tracking the second target of interest, and displaying the first secondary stream including the portion of the first and second targets of interest.

Automatic extraction of secondary video streams

A system is disclosed that obtains an image of an apparel item and text describing the apparel item. The system also determines descriptive metadata of the apparel item by extracting information from the text. Further, the system further determines categorization metadata of the apparel item by analyzing the image. Additionally, the system stores the image, the descriptive metadata, and the categorization metadata in a virtual closet. Moreover, the system provides a recommendation of an outfit from a stylist to a user based on the virtual closet.

System and method for social style mapping

The automatic generation of one or more secondary video streams based on an input primary video stream may use analytics performed on the video to provide information on targets, events, and/or areas of interest to permit the one or more secondary video streams to concentrate on one or more targets, events and/or areas of interest.

A monitoring method and system are disclosed. In one embodiment, a method includes monitoring the health of a person in a home. The method includes capturing a video sequence from a first camera disposed within the home, including capturing two-dimensional image data for the video sequence; receiving depth data corresponding to the two-dimensional data, and associating the depth data with the video sequence as metadata; setting a plurality of events to monitor associated with the person, the events defined to include actions captured from the first camera, at least a first event including the person's body being in a particular bodily position and performing video content analysis on the video sequence to determine whether the events have occurred. The video content analysis includes automatically detecting a potential human object from the video sequence based on the two-dimensional image data; using the depth data to determine a size and bodily position of the potential human object; and based on the size of the potential object, confirming that the potential human object is an actual human, thereby confirming the potential human object as a target. The method further includes determining that the first event has occurred based on the determined bodily position of the target.

System and method for home health care monitoring

A content analysis engine receives video input and performs analysis of the video input to produce one or more gross change primitives. A view engine coupled to the content analysis engine receives the one or more gross change primitives from the content analysis engine and provides view identification information. A rules engine coupled to the view engine receives the view identification information from the view engine and provides one or more rules based on the view identification information. An inference engine performs video analysis based on the one or more rules provided by the rules engine and the one or more gross change primitives.

View handling in video surveillance systems

A video surveillance system extracts video primitives and extracts event occurrences from the video primitives using event discriminators. The system can undertake a response, such as an alarm, based on extracted event occurrences.

Video surveillance system employing video primitives

A method and system for performing video content analysis based on two-dimensional image data and depth data are disclosed. Video content analysis may be performed on the two-dimensional image data, and then the depth data may be used along with the results of the video content analysis of the two-dimensional data for tracking and event detection.

System and method for video content analysis using depth sensing

A method and system for monitoring a retail environment by performing video content analysis based on two-dimensional image data and depth data are disclosed. Accuracy in customer actions to provide assistance, change marketing behavior, safety and theft, for example, is increase by analyzing video containing two-dimensional image data and associated depth data. Height data may be obtained from depth data to assist in object detection, object classification (e.g., detection a customer or inventory) and/or event detection.

System and method for monitoring a retail environment using video content analysis with depth sensing

A method and system for monitoring buildings (including houses and office buildings) by performing video content analysis based on two-dimensional image data and depth data are disclosed. Occupation and use of such buildings may be monitored with higher accuracy to provide higher energy efficiency usage, to control operation of components therein, and/or provide better security. Height data may be obtained from depth data to provide greater reliability in object detection, object classification and/or event detection.

System and method for building automation using video content analysis with depth sensing

The automatic generation () of one or more secondary video streams based on an input primary video stream () may use analytics performed on the video to provide information on targets, events, and/or areas of interest to permit the one or more secondary video streams () to concentrate on one or more targets, events, and/or areas of interest.

A system and method of analyzing a plurality of video signals received concurrently from a plurality of video cameras includes receiving a plurality of video signals captured from a plurality of respective video cameras, each video signal received over a respective camera channel; multiplexing the plurality of video signals to output a multiplexed video signal including frames from each of the captured video signals; performing automated video content analysis on the multiplexed video signal, including performing automated video content analysis for at least two of the camera channels.

Multichannel video content analysis system using video multiplexing

A home health care monitoring method and system are disclosed. In one embodiment, a method includes: capturing a plurality of video sequences from a plurality of respective cameras disposed in different locations within a patient's home, including capturing two-dimensional image data and depth data for each video sequence; defining a plurality of events to monitor associated with the patient, the events including, during a predetermined time period, at least (1) the patient's body being in a particular position for at least a predetermined amount of time; performing depth-enhanced video content analysis on the plurality of video sequences to determine whether the event (1) has occurred. The video content analysis may include: for each of at least two cameras: automatically detecting a human object based on the two-dimensional image data; using the depth data to confirm whether the human object is in the particular position; and based on the confirmation, tracking an amount of time that the human object is in the particular position; and then determining that the event (1) has occurred based on the collective tracked amount of time.

Growing community of inventors

Ashburn, VA, United States of America

Gary W Myers