Avigilon Fortress Corporation

Objectvideo, Inc

Samsung Electronics Co., Ltd.

Diamondback Vision, Inc.

John I W Clark

Alan J Lipton

Andrew J Chosak

Gary W Myers

Zhong Zhang

Peter L Venetianer

Niels Haering

W Andrew Scanlon

Robert A Cutting

Péter L Venetianer

Matthew F Frazier

James S Sfekas

Weihong Yin

Paul C Brewer

Donald Gerard Madden

Li Yu

Geoffrey Egnal

Michael C Mannebach

Steve Titus

Thomas M Strat

Mark Allmen

William E Severson

James Morris

Tasuki Hirata

Bruce Thompson

Amit Mistry

Haiying Liu

Thomas E Slowe

Chung-Cheng Yen

Wei Hong Yin

Pramod Kalapa

Caspar Horne

John F Tilki

Kelly Schomburg

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 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

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 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 sensing device includes: a video imager to obtain a video; a processing unit to receive and process the video from the video imager; and a communication channel to output non-imagery signals.

Video imagery-based sensor

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 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.

A method of video processing may include analyzing input video information to determine if a current video frame is directed to a same view as a previous video frame; determining whether a new view is present; and indicating a need to use video processing information pertaining to the new view if a new view is determined to be present.

A sensing device includes: a video imager to obtain video of an area; a processing unit to receive and process the video from the video imager to identify one of an event or a datum of interest; and a communication channel to output a non-imagery signal representing the event or datum.

A machine-accessible medium containing software code that, when read by a computer, causes the computer to perform method for filtering a video in a video system. The method may include generating a foreground mask and a background model from a video, where the foreground mask comprises moving pixels in the video and the background model comprises a statistical description, including a mean and a variance value, for each pixel in the video. Then the background model variance is filtered with a one-dimensional high pass filter in a single orientation. Next, for each linear grouping of pixels in the single orientation of the filtered variance, a first edge pixel is detected between a high-variance group of pixels and a low-variance group of pixels. Then a first waterline position is detected as an area bounded by the first edge pixels.

Tide filtering for video surveillance system

A system detects an object in frames of a video sequence to obtain a detected object, tracks the detected object in the frames of the video sequence to obtain a tracked object, and classifies the tracked object as a real object or a spurious object based on spatial and/or temporal properties of the tracked object.

Spurious object detection in a video surveillance system

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Flamborough, Canada

John I W Clark