Objectvideo, Inc

Diamondback Vision, Inc.

Mark Allmen

Thomas M Strat

Alan J Lipton

Niels Haering

William E Severson

Zhong Zhang

Peter L Venetianer

Andrew J Chosak

Matthew F Frazier

Tasuki Hirata

John I W Clark

James S Sfekas

Thomas E Slowe

Paul C Brewer

Pèter L Venetianer

Chris Debrunner

Caspar Horne

John F Tilki

James S Seekas

John Clark

A video surveillance system is set up, calibrated, tasked, and operated. The system extracts video primitives and extracts event 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

A video surveillance system is set up, calibrated, tasked, and operated. The 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 sequence is encoded, where the video sequence includes a background composite and foreground regions. The video sequence is encoded based on balancing bits per pixel for the background composite with bits per pixel for the foreground regions.

Bit-rate allocation system for object-based video encoding

A method for segmenting video data into foreground and background portions utilizes statistical modeling of the pixels. A statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons. The method for segmenting video data may be further incorporated into a method for implementing an intelligent video surveillance system.

Video segmentation using statistical pixel modeling

A method for video background scene maintenance uses statistical pixel modeling. A background statistical model is built based on the input video. A background image may be built based on the background statistical model, or video segmentation into foreground and background may be carried out based on the background statistical model.

Video scene background maintenance using statistical pixel modeling

A method of generating and utilizing a scene model from a sequence of video frames produces a three-dimensional scene model, useful for video processing. The method separates foreground and background data. It uses an estimate of relative motion of an observer to project each frame onto a coordinate system of the three-dimensional scene model. It then merges the background data of a given frame into the scene model.

Scene model generation from video for use in video processing

A method for implementing a video tripwire includes steps of calibrating a sensing device to determine sensing device parameters for use by the system; initializing the system, including entering at least one virtual tripwire; obtaining data from the sensing device; analyzing the data obtained from the sensing device to determine if the at least one virtual tripwire has been crossed; and triggering a response to a virtual tripwire crossing.

Video tripwire

A method for segmenting video data into foreground and background portions utilizes statistical modeling of the pixels. A statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons.

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Morrison, CO, United States of America

Mark Allmen