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Visual Object Tracking Based on Color and Implicit Shape Features

MeanShift and its adaptive version CamShift have received wide attention as an efficient and robust method for object tracking. Both methods try to reach the peak of the probability distribution using gradient ascent approach, and thus they are easily prone to local maxima. This paper presents a novel tracking algorithm based on the CamShift framework. It first removes small local peaks with a dominant color filter to reduce the possibility of the trap to local maxima. Next, our method embeds the color and implicit shape features of the target object into the probability distribution image. Finally, it computes the moments of the probability distribution image frame by frame and then estimates the center and scale of the tracking object accordingly. Experiments on five challenging video databases indicate the proposed algorithm performs significantly better than the conventional CamShift and MeanShift based algorithms.