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