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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