Object tracking by co-trained classifiers and particle filters

Liang Tang; Shanqing Li; Keyan Liu; Lei Wang

doi:10.1117/12.840139

18 January 2010 Object tracking by co-trained classifiers and particle filters

Liang Tang, Shanqing Li, Keyan Liu, Lei Wang

Proceedings Volume 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques; 753909 (2010) https://doi.org/10.1117/12.840139
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

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.

Citation Download Citation

Liang Tang, Shanqing Li, Keyan Liu, and Lei Wang "Object tracking by co-trained classifiers and particle filters", Proc. SPIE 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques, 753909 (18 January 2010); https://doi.org/10.1117/12.840139

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KEYWORDS

Particle filters

Particles

Detection and tracking algorithms

Error analysis

Feature extraction

Statistical modeling

Video

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