3D arm movement tracking using adaptive particle filter

Authors:
Feng Guo;Gang Qian
Affiliations:
Motorola Applied Research and Technology Center;Department of Electrical Engineering, Arizona State University
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 1
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Parametric correspondence and chamfer matching: two new techniques for image matching

IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2

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Abstract

In this paper, we present a monocular 3D arm movement tracking system using adaptive particle filter. The effective sample size (ESS) is analyzed in the adaptive particle filter to tackle the abrupt dynamic changes of the arm movement. Sample-efficiency-optimized auxiliary particle filter (SEOAPF) is invoked when low ESS is detected. In SEO-APF, the auxiliary variable weights are computed to minimize the true importance weight variance, so the tracking results and the efficiency of the particle filters are improved. Experimental results have demonstrated the efficacy of this approach for 3D arm movement tracking.