Efficient visual tracking using particle filter with incremental likelihood calculation

Authors:
Huaping Liu;Fuchun Sun
Affiliations:
Department of Computer Science and Technology, Tsinghua University, Beijing, China and State Key Laboratory of Intelligent Technology and Systems, Beijing, China and Tsinghua National Laboratory f ...;Department of Computer Science and Technology, Tsinghua University, Beijing, China and State Key Laboratory of Intelligent Technology and Systems, Beijing, China and Tsinghua National Laboratory f ...
Venue:
Information Sciences: an International Journal
Year:
2012

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Abstract

In this paper, we propose a particle filter that determines the weight of each particle employing the incremental likelihood calculation. Since there is usually a large overlap region between the two particles that are sequentially generated, determining the weight of the particle has only a small time cost. Therefore, the real-time performance of the proposed tracker can be dramatically improved. Extensive experimental results for single-object and multiple-object tracking scenarios are presented to demonstrate the efficiency of the proposed approach. Finally, an interesting color-based active vision system is developed for a free-floating space robot testbed to facilitate teleoperation.