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We propose a novel scheme that jointly employs anisotropic mean shift and particle filters for tracking moving objects from video. The proposed anisotropic mean shift, that is applied to partitioned areas in a candidate object bounding box whose parameters (center, width, height and orientation) are adjusted during the mean shift iterations, seeks multiple local modes in spatial-kernel weighted color histograms. By using a Gaussian distributed Bhattacharyya distance as the likelihood and mean shift updated parameters as the state vector, particle filters become more efficient in terms of tracking using a small number of particles (<20). The combined scheme is able to maintain the merits of both methods. Experiments conducted on videos containing deformable objects with long-term partial occlusions and intersections have shown robust tracking performance. Comparisons with two existing methods have been made which showed marked improvement in terms of robustness to occlusions, tightness and accuracy of tracked box, and tracking drift.