Optical flow analysis and Kalman Filter tracking in video surveillance algorithms

Optical flow analysis and Kalman Filter tracking in video surveillance algorithms

A SIMULINK-based algorithm for monitoring contacts in a surveillance video sequence using Optical Flow Analysis and Kalman Filters was developed. The Horn-Schunk Optical Flow Algorithm was used to identify contacts in a surveillance video sequence. The position and behavior of these contacts was...

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Bibliographic Details
Main Author: Semko, David A.
Other Authors: Fargues, Monique P.
Published: Monterey California. Naval Postgraduate School 2012
Online Access:http://hdl.handle.net/10945/3521
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-35212014-11-27T16:04:39Z Optical flow analysis and Kalman Filter tracking in video surveillance algorithms Semko, David A. Fargues, Monique P. Cristi, Roberto Naval Postgraduate School (U.S.) A SIMULINK-based algorithm for monitoring contacts in a surveillance video sequence using Optical Flow Analysis and Kalman Filters was developed. The Horn-Schunk Optical Flow Algorithm was used to identify contacts in a surveillance video sequence. The position and behavior of these contacts was monitored by a modification of the traditional Kalman Filter. The Kalman Filter algorithm implemented has the ability to track up to ten contacts at a time, correctly assigning each of a maximum ten filters to their respective contacts on a frame-by-frame basis. Initial tests using artificial data show good performance of both the Optical Flow Analysis algorithm and the Kalman Filter Tracking algorithm. Surveillance video data was also used to test the algorithm with promising results. 2012-03-14T17:38:36Z 2012-03-14T17:38:36Z 2007-06 Thesis http://hdl.handle.net/10945/3521 156936523 Approved for public release, distribution unlimited Monterey California. Naval Postgraduate School
collection NDLTD
sources NDLTD
description A SIMULINK-based algorithm for monitoring contacts in a surveillance video sequence using Optical Flow Analysis and Kalman Filters was developed. The Horn-Schunk Optical Flow Algorithm was used to identify contacts in a surveillance video sequence. The position and behavior of these contacts was monitored by a modification of the traditional Kalman Filter. The Kalman Filter algorithm implemented has the ability to track up to ten contacts at a time, correctly assigning each of a maximum ten filters to their respective contacts on a frame-by-frame basis. Initial tests using artificial data show good performance of both the Optical Flow Analysis algorithm and the Kalman Filter Tracking algorithm. Surveillance video data was also used to test the algorithm with promising results.
author2 Fargues, Monique P.
author_facet Fargues, Monique P.
Semko, David A.
author Semko, David A.
spellingShingle Semko, David A.
Optical flow analysis and Kalman Filter tracking in video surveillance algorithms
author_sort Semko, David A.
title Optical flow analysis and Kalman Filter tracking in video surveillance algorithms
title_short Optical flow analysis and Kalman Filter tracking in video surveillance algorithms
title_full Optical flow analysis and Kalman Filter tracking in video surveillance algorithms
title_fullStr Optical flow analysis and Kalman Filter tracking in video surveillance algorithms
title_full_unstemmed Optical flow analysis and Kalman Filter tracking in video surveillance algorithms
title_sort optical flow analysis and kalman filter tracking in video surveillance algorithms
publisher Monterey California. Naval Postgraduate School
publishDate 2012
url http://hdl.handle.net/10945/3521
work_keys_str_mv AT semkodavida opticalflowanalysisandkalmanfiltertrackinginvideosurveillancealgorithms
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