A multiple feature based particle filter using mutual information maximization

Kihyun Hong; Kyuseo Han

doi:10.1117/12.872471

24 January 2011 A multiple feature based particle filter using mutual information maximization

Kihyun Hong, Kyuseo Han

Proceedings Volume 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques; 78780F (2011) https://doi.org/10.1117/12.872471
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

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.

Citation Download Citation

Kihyun Hong and Kyuseo Han "A multiple feature based particle filter using mutual information maximization", Proc. SPIE 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, 78780F (24 January 2011); https://doi.org/10.1117/12.872471

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KEYWORDS

Particle filters

Detection and tracking algorithms

Particles

Process modeling

Algorithm development

Signal to noise ratio

Visual process modeling

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