This paper proposes a more robust and efficient Mean Shift object tracking algorithm which is optimized for
embedded multicore DSP Parallel system. Firstly, the RGB image is transformed into HSV image which is robust in
many aspects such as lighting changes. Then, the color histogram model is used in the back projection process to
generate the color probability distribution. Secondly, the size and position of search window are initialized in the first
frame, and Mean Shift algorithm calculates the center position of the target and adjusts the search window automatically
both in size and location, according to the result of the previous frame. Finally, since the multicore DSP system is
commonly adopted in the embedded application such as seeker and an optical scout system, we implement the proposed
algorithm in the TI multicore DSP system to meet the need of large amount computation. For multicore parallel
computing, the explicit IPC based multicore framework is designed which outperforms OpenMP standard. Moreover,
the parallelisms of 8 functional units and cross path data fetch capability of C66 core are utilized to accelerate the
computation of iteration in Mean Shift algorithm. The experimental results show that the algorithm has good
performance in complex scenes such as deformation, scale change and occlusion, simultaneously the proposed
optimization method can significantly reduce the computation time.
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