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This article is part of the series Video Tracking in Complex Scenes for Surveillance Applications.

Open Access Research Article

Efficient Adaptive Combination of Histograms for Real-Time Tracking

F Bajramovic1*, B Deutsch2, Ch Gräßl2 and J Denzler1

Author Affiliations

1 Department of Mathematics and Computer Science, Friedrich-Schiller University Jena, 07737 Jena, Germany

2 Computer Science Department 5, University of Erlangen-Nuremberg, 91058 Erlangen, Germany

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EURASIP Journal on Image and Video Processing 2008, 2008:528297  doi:10.1155/2008/528297

The electronic version of this article is the complete one and can be found online at: http://jivp.eurasipjournals.com/content/2008/1/528297


Received:30 October 2007
Revisions received:14 March 2008
Accepted:12 July 2008
Published:16 July 2008

© 2008 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

We quantitatively compare two template-based tracking algorithms, Hager's method and the hyperplane tracker, and three histogram-based methods, the mean-shift tracker, two trust-region trackers, and the CONDENSATION tracker. We perform systematic experiments on large test sequences available to the public. As a second contribution, we present an extension to the promising first two histogram-based trackers: a framework which uses a weighted combination of more than one feature histogram for tracking. We also suggest three weight adaptation mechanisms, which adjust the feature weights during tracking. The resulting new algorithms are included in the quantitative evaluation. All algorithms are able to track a moving object on moving background in real time on standard PC hardware.

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