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A new scheme for back-projection of weights for mean shift based object tracking is proposed. Weights are calculated based on relative counts of histogram bins for each feature used in similarity assessment. A fusion scheme is proposed to combine the back-projected weights from different features, such that the dissimilarities between the object being tracked and the background are boosted. A mechanism is proposed to calculate the overall similarity between reference and candidate windows, without actually doing the back-projection, and just by few computations based on histogram bin counts. Moreover, the reference and candidate windows do not need to have the same size. The proposed scheme shows better tracking results compared to the traditional mean shift based tracking, especially in case of fast object movement and high background clutter.