Abstract: The Continuously Adaptive Mean-Shift (CamShift)
algorithm, incorporating scene depth information is combined with
the l1-minimization sparse representation based method to form a
hybrid kernel and state space-based tracking algorithm. We take
advantage of the increased efficiency of the former with the
robustness to occlusion property of the latter. A simple interchange
scheme transfers control between algorithms based upon drift and
occlusion likelihood. It is quantified by the projection of target
candidates onto a depth map of the 2D scene obtained with a low cost
stereo vision webcam. Results are improved tracking in terms of drift
over each algorithm individually, in a challenging practical outdoor
multiple occlusion test case.