A robust visual tracking method which can be used in complex environments is presented in this paper. The color cue
and the shape cue are utilized to represent the target and fused together by democratic integration method. The multi-cue
object representation is incorporated into the framework of particle filter which is a powerful probabilistic method for
visual tracking. To each sample of the particle filter a mean shift operation is applied, which make the samples more
effective such that the number of particles needed is significantly decreased. Unlike regular mean shift, in our method the
number of mean shift iterations is limited according to the reliability of the color cue for two purposes. One is to prevent
the particles from being misled by mean shift when the color cue is unreliable. The other is to reduce the waste of
computation. Experimental results show that our method greatly improves the robustness and reduces the computational
cost compared with the state-of-art methods.
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