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Proceedings Paper

Object tracking by co-trained classifiers and particle filters
Author(s): Liang Tang; Shanqing Li; Keyan Liu; Lei Wang
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Paper Abstract

This paper presents an online object tracking method, in which co-training and particle filters algorithms cooperate and complement each other for robust and effective tracking. Under framework of particle filters, the semi-supervised cotraining algorithm is adopted to construct, on-line update, and mutually boost two complementary object classifiers, which consequently improves discriminant ability of particles and its adaptability to appearance variants caused by illumination changing, pose verying, camera shaking, and occlusion. Meanwhile, to make sampling procedure more efficient, knowledge from coarse confidence maps and spatial-temporal constraints are introduced by importance sampling. It improves not only the accuracy and efficiency of sampling procedure, but also provides more reliable training samples for co-training. Experimental results verify the effectiveness and robustness of our method.

Paper Details

Date Published: 18 January 2010
PDF: 10 pages
Proc. SPIE 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques, 753909 (18 January 2010); doi: 10.1117/12.840139
Show Author Affiliations
Liang Tang, Hewlett-Packard Labs. China (China)
Shanqing Li, Beijing Institute of Technology (China)
Keyan Liu, Hewlett-Packard Labs. China (China)
Lei Wang, Hewlett-Packard Labs. China (China)


Published in SPIE Proceedings Vol. 7539:
Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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