Abstract To improve the performance of sparse representation based trackers, an object tracking method based on global and local structural inverse sparse appearance model is proposed. Firstly, an inverse sparse representation formulation is proposed to compute the weights of all particles by solving one optimization problem and this is conducive to improving the real-time performance. Then, a ranking mechanism based on joint discriminative similarity map(JDS map) is designed to improve the robustness. The formulation block candidates are divided into several pitches and the weighted sparse solutions are computed respectively. Next, these pitches are concatenated with different weights and meanwhile the sparse solution of each particle is computed. A combination mechanism is proposed to unite two sparse solutions as the JDS map. During the tracking, the object template and weight template are updated using a double-template updating strategy. Experiments demonstrate that the proposed algorithm is robust for benchmark video sequences under complicated conditions.
Fund:Supported by National Natural Science Foundation of China (No.61071199), Natural Science Foundation of Hebei Province (No.F2016203422)
About author:: (HU Zhengping(Corresponding author), born in 1970, Ph.D., professor. His research interests include pattern recognition theory and its applications.)(XIE Ronglu, born in 1992, master student. Her research interests include target tracking.) (WANG Meng, born in 1982, Ph.D. candidate. Her research interests include pattern recognition and sparse representation classification.)(SUN Zhe, born in 1990, Ph.D. candidate. Her research interests include sparse representation classification.)
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2016, Vol. 29 
