Target Tracking Based on Biological-Like Vision Identity via Improved Sparse Representation and Particle Filtering

To effectively track targets under partial occlusion and illumination variation, an improved target tracking method based on combination of sparse representation and particle filtering is proposed in this paper. We regard the candidate target particle set as redundant dictionary and the target template as observation signal to reduce the computational complexity and enhance the real-time performance of target tracking. Besides, to enhance tracking robustness for better adaption to illumination and occlusion, the density histogram, local binary pattern feature fusion, trivial templates and energy control parameters are also utilized in this study. Finally, extensive simulation experiments under different circumstances show that the proposed method performs better compared with other methods, and the average computation time decreases greatly.