Avigilon Fortress Corporation

Objectvideo, Inc

University of Central Florida Research Foundation, Inc.

Zeeshan Rasheed

Zhong Zhang

Niels Haering

Alan J Lipton

Weihong Yin

Omar Javed

Li Yu

Peter L Venetianer

Andrew J Chosak

Haiying Liu

Himaanshu Gupta

Allison Beach

Gary W Myers

Dana Eubanks

Kyle Glowacki

Donald Gerard Madden

Gang Qian

Paul C Brewer

Khurram Hassan-Shafique

Geoffrey R Taylor

Xiao Chun Cao

Péter L Venetianer

Mubarak Shah

Andrew J Martone

Mun Wai Lee

Wei Hong Yin

Pèter L Venetianer

Asaad Hakeem

Liangyin Yu

Khurram Shafique

Systems, methods and computer-readable media for creating and using video analysis rules that are based on map data are disclosed. A sensor(s), such as a video camera, can track and monitor a geographic location, such as a road, pipeline, or other location or installation. A video analytics engine can receive video streams from the sensor, and identify a location of the imaged view in a geo-registered map space, such as a latitude-longitude defined map space. A user can operate a graphical user interface to draw, enter, select, and/or otherwise input on a map a set of rules for detection of events in the monitored scene, such as tripwires and areas of interest. When tripwires, areas of interest, and/or other features are approached or crossed, the engine can perform responsive actions, such as generating an alert and sending it to a user.

Systems and methods for video analysis rules based on map data

Embodiments relate to systems, devices, and computer-implemented methods for performing automated visual surveillance by obtaining video camera coordinates determined using video data, video camera metadata, and/or digital elevation models, obtaining a surveillance rule associated with rule coordinates, identifying a video camera that is associated with video camera coordinates that include at least part of the rule coordinates, and transmitting the surveillance rule to a computing device associated with the video camera. The rule coordinates can be automatically determined based on received coordinates of an object. Additionally, the surveillance rule can be generated based on instructions from a user in a natural language syntax.

Systems and methods for automated visual surveillance

A method for determining a likelihood that a first object captured in a first image and a second object captured in a second image are the same object includes capturing the first image from a first viewpoint and a second image from a second viewpoint, wherein the first object is in the first image, and the second object is in the second image. The method also includes determining a first likelihood that a first visual feature on the first object and a second visual feature on the second object are the same visual feature, and determining a second likelihood that a dimension of the first object and a corresponding dimension of the second object are the same. The method then includes determining a final likelihood that the first object and the second object are the same object based at least partially upon the first likelihood and the second likelihood.

Automatic moving object verification

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 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

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. The method for segmenting video data may be implemented in hardware.

Video segmentation using statistical pixel modeling

Method for finding paths in video

A method for segmenting video data into foreground and background () portions utilizes statistical modeling of the pixels &#x39b; 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 The method for segmenting video data may be implemented in hardware.

A video sequence of a field of view within an environment is received. Targets are detected in the video sequence. Target geo-positional information is received. Correspondences between the targets detected in the video sequence and the target geo-positional information are determined and used to calibrate the camera and to geo-register a field of view of the camera.

Automatic camera calibration and geo-registration using objects that provide positional information

A method of video analysis may comprise tracking a state of each target in a video through multiple frames of said video, each state indicating a visibility condition of a target.

Multi-state target tracking

A computer-based method for automatic detection of water regions in a video include the steps of estimating a water map of the video and outputting the water map to an output medium, such as a video analysis system. The method may further include the steps of training a water model from the water map; re-classifying the water map using the water model by detecting water pixels in the video; and refining the water map.

Method for detecting water regions in video

A technique for video processing includes: receiving video from an overhead view of a scene; detecting moving pixels in the video; detecting line segments in the video based on detected moving pixels; identifying targets in the video based on the detected line segments; tracking targets in the video based on the identified targets; and managing tracked targets in the video.

Target detection and tracking from overhead video streams

A computer-readable medium contains software that, when read by a computer, causes the computer to perform a method for wide-area site-based surveillance. The method includes receiving surveillance data, including view targets, from a plurality of sensors at a site; synchronizing the surveillance data to a single time source; maintaining a site model of the site, wherein the site model comprises a site map, a human size map, and a sensor network model; analyzing the synchronized data using the site model to determine if the view targets represent a same physical object in the site; creating a map target corresponding to a physical object in the site, wherein the map target includes at least one view target; receiving a user-defined global event of interest, wherein the user-defined global event of interest is based on the site map and based on a set of rules; detecting the user-defined global event of interest in real time based on a behavior of the map target; and responding to the detected event of interest according to a user-defined response to the user-defined global event of interest.

Growing community of inventors

Herndon, VA, United States of America

Zeeshan Rasheed