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Proceedings Paper

A multiple feature based particle filter using mutual information maximization
Author(s): Kihyun Hong; Kyuseo Han
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Paper Abstract

In designing a tracking algorithm, utilizing several different features, e.g., color histogram, gradient histogram and other object descriptors, is preferable to increase robustness of tracking performance. In this paper, we propose a multiple feature fusion framework to improve the tracking by assigning appropriate weights to individual features. The feature weights are optimally obtained by a waterfilling procedure that maximizes mutual information between target object features and query features. Especially, in this paper, we focus on a particle filter tracking implementation of the multiple feature fusion framework. Our experiments show that object tracking with multiple features outperforms single feature based tracking methods and illustrates that the proposed optimal feature weighting increases robustness of multiple-feature based tracking performance.

Paper Details

Date Published: 24 January 2011
PDF: 9 pages
Proc. SPIE 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, 78780F (24 January 2011); doi: 10.1117/12.872471
Show Author Affiliations
Kihyun Hong, Purdue Univ. (United States)
Kyuseo Han, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 7878:
Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques
Juha Röning; David P. Casasent; Ernest L. Hall, Editor(s)

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