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In this paper, we propose a novel video object tracking approach based on kernel density estimation and Markov random field (MRF). The interested video objects are first segmented by the user, and a nonparametric model based on kernel density estimation is initialized for each video object and the remaining background, respectively. A temporal saliency map is also initialized for each object to memorize the temporal trajectory. Based on the probabilities evaluated on the non-parametric models, each pixel in the current frame is first classified into the corresponding video object or background using the maximum likelihood criterion. Starting from the initial classification result, a MRF model that combines spatial smoothness and temporal coherency is selectively exploited to generate more reliable video objects. The nonparametric model and the temporal saliency map for each video object are updated and propagated for the future tracking. Experimental results on several MPEG-4 test sequences demonstrate the good segmentation performance of our approach.