Objectvideo, Inc

Avigilon Fortress Corporation

Affectiva, Inc.

Diamondback Vision, Inc.

Blubox Security, Inc.

Niels Haering

Alan J Lipton

Andrew J Chosak

Zhong Zhang

Peter L Venetianer

Weihong Yin

Gary W Myers

Li Yu

Paul C Brewer

Zeeshan Rasheed

Matthew F Frazier

Donald Gerard Madden

Omar Javed

Himaanshu Gupta

John I W Clark

Geoffrey Egnal

Mark Allmen

James S Sfekas

Haiying Liu

Thomas M Strat

William E Severson

Rana El Kaliouby

Péter L Venetianer

Mun Wai Lee

Michael C Mannebach

Geoffrey R Taylor

Khurram Hassan-Shafique

Youssef Kashef

Viprali Bhatkar

Ahmed Adel Osman

Tasuki Hirata

Robert A Cutting

Tae Eun Choe

Thomas E Slowe

Kiran Gunda

Xiao Chun Cao

Pèter L Venetianer

John Ian Wallace Clark

W Andrew Scanlon

Yongtong Hu

Andrew J Martone

Patrick J Barry

Thibaud Senechal

Patrick E Barry

Atul Kanaujia

Chung-Cheng Yen

Wei Hong Yin

Pramod Kalapa

Francis John Cusack, Jr

Luciano Eloy Barrios

Soma Biswas

Asaad Hakeem

James S Seekas

John Clark

Liangyin Yu

Caspar Horne

John F Tilki

Video of one or more vehicle occupants is obtained and analyzed. Heart rate information is determined from the video. The heart rate information is used in cognitive state analysis. The heart rate information and resulting cognitive state analysis are correlated to stimuli, such as digital media, which is consumed or with which a vehicle occupant interacts. The heart rate information is used to infer cognitive states. The inferred cognitive states are used to output a mood measurement. The cognitive states are used to modify the behavior of a vehicle. The vehicle is an autonomous or semi-autonomous vehicle. Training is employed in the analysis. Machine learning is engaged to facilitate the training. Near-infrared image processing is used to obtain the video. The analysis is augmented by audio information obtained from the vehicle occupant.

Autonomous vehicle control using heart rate collection based on video imagery

A system and method for video surveillance and searching are disclosed. Video is analyzed and events are automatically detected. Based on the automatically detected events, textual descriptions are generated. The textual descriptions may be used to supplement video viewing and event viewing, and to provide for textual searching for events.

Automatic event detection, text generation, and use thereof

A method to determine a base focal length of a pan-tilt-zoom (PTZ) camera system. The method includes generating a spherical mosaic with a feature track by the camera system and determining, by the camera system, a base focal length by analyzing a conic trajectory created by the feature track on an image plane.

Automatic calibration of PTZ camera system

Video of one or more people is obtained and analyzed. Heart rate information is determined from the video. The heart rate information is used in mental state analysis. The heart rate information and resulting mental state analysis are correlated to stimuli, such as digital media, which is consumed or with which a person interacts. The heart rate information is used to infer mental states. The inferred mental states are used to output a mood measurement. The mental state analysis, based on the heart rate information, is used to optimize digital media or modify a digital game. Training is employed in the analysis. Machine learning is engaged to facilitate the training.

Mental state mood analysis using heart rate collection based on video imagery

Provided are a device, system, and method for multi-factor multi-biometric access control that authenticate a user based on at least one of what the user knows, what the user possesses and what unique physical, biological and physiological or biometric traits the user has, wherein the device includes a single sensor from which multi-biometric data is derived.

Multi-factor and multi-mode biometric physical access control device

A system for detecting behavior of a target may include: a target detection engine, adapted to detect at least one target from one or more objects from a video surveillance system recording a scene; a path builder, adapted to create at least one mature path model from analysis of the behavior of a plurality of targets in the scene, wherein the at least one mature path model includes a model of expected target behavior with respect to the at least one path model; and a target behavior analyzer, adapted to analyze and identify target behavior with respect to the at least one mature path model. The system may further include an alert generator, adapted to generate an alert based on the identified behavior.

Video processing system using target property map

A video surveillance system, device and methods may accurately model the shape of a human object monitored by a video stream. 3D human models, such as a coarse 3D human model and a detailed 3D human model may be estimated by mapping individual body part components to a frame. For example, a coarse 3D human model may be obtained by mapping the cylindrical body parts to a plurality of skeleton pose estimates on a part by part basis. A detailed 3D human model may be estimated by mapping detailed human body parts to respective the cylindrical body parts of the coarse 3D human model on a part by part basis. The detailed 3D human model may be used to detect accessories of the human object being monitored, as well as overall dimensions, body part dimensions, age, and gender of the human object being monitored.

Video surveillance systems, devices and methods with improved 3D human pose and shape modeling

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 video surveillance system may include at least one sensing unit capable of being operated in a scanning mode and a video processing unit coupled to the sensing unit, the video processing unit to receive and process image data from the sensing unit and to detect scene events and target activity.

Scanning camera-based video surveillance system

Mental state analysis is performed by obtaining video of an individual as the individual interacts with a computer, either by performing various operations or by consuming a media presentation. The video is analyzed to determine eye-blink information on the individual, such as eye-blink rate or eye-blink duration. A mental state of the individual is then inferred based on the eye blink information. The blink-rate information and associated mental states can be used to modify an advertisement, a media presentation, or a digital game.

Mental state analysis using blink rate

Video of one or more people is obtained and analyzed. Heart rate information is determined from the video and the heart rate information is used in mental state analysis. The heart rate information and resulting mental state analysis are correlated to stimuli, such as digital media which is consumed or with which a person interacts. The heart rate information is used to infer mental states. The mental state analysis, based on the heart rate information, can be used to optimize digital media or modify a digital game.

Mental state analysis using heart rate collection based on video imagery

A video camera may overlook a monitored area from any feasible position. An object flow estimation module monitor the moving direction of the objects in the monitored area. It may separate the consistently moving objects from the other objects. A object count estimation module may compute the object density (e.g. crowd). A object density classification module may classify the density into customizable categories.

Object density estimation in video

Growing community of inventors

Reston, VA, United States of America

Niels Haering