Avigilon Fortress Corporation

Objectvideo, Inc

Samsung Electronics Co., Ltd.

Motorola Solutions, Inc.

W Andrew Scanlon

Alan J Lipton

Zhong Zhang

Weihong Yin

Li Yu

Peter L Venetianer

Andrew J Chosak

Gary W Myers

Robert A Cutting

Paul C Brewer

John I W Clark

Dana Eubanks

Himaanshu Gupta

Yongtong Hu

Niels Haering

Péter L Venetianer

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 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

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

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.

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 method of monitoring a point of sale (POS) transaction including: receiving non video data regarding the POS transaction; defining exceptional transaction rules based at least on predetermined non video POS data events; inferring a potentially exceptional transaction based on a corresponding exceptional transaction rule and the received POS non video data; and verifying the inferred potentially exceptional transaction with video data regarding a corresponding POS transaction.

A system for video monitoring a retail business process includes a video analytics engine to process video obtained by a video camera and generate video primitives regarding the video, A user interface is used to define at least one activity of interest regarding an area being viewed, each activity of interest identifying at least one of a rule or a query regarding the area being viewed. An activity inference engine processes the generated video primitives based on each defined activity of interest to determine if an activity of interest occurred in the video.

Video analytics for retail business process monitoring

Non video data regarding a point of sale (POS) transaction is received. POS primitives are generated based on the received non video POS data. Video data regarding a corresponding POS transaction is received. Video primitives are generated based on the received video data. An exceptional transaction is inferred based on a corresponding exceptional transaction rule and at least one of the generated POS primitives or video primitives.

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Falls Church, VA, United States of America

W Andrew Scanlon