The optimization of improved Mean Shift object tracking in embedded multicore DSP parallel system

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.