Abstract：Due to the single feature space used in standard mean-shift algorithm, the confusion caused by the similarity object in its vicinity is hard to deal with. A variety of local pixel-level features are summarized, and a method for measuring the discrimination of various features is presented to make feature selection adaptive. Based on the analysis of deriving the weights during the iteration, an improved mean shift algorithm is proposed based on the discrimination measurement of various features through multi-feature space. It monitors the saliency of each feature effectively to compensate each other and improves the robustness to the confusion caused by the outlier. Experimental results indicate the proposed algorithm is real-time and robust and it has good tracking performance on object tracking.
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2009, Vol. 22 
