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.
胡正平,谢荣路,王蒙,孙哲. 基于全局与局部结构反稀疏外观模型的目标跟踪算法*[J]. 模式识别与人工智能, 2016, 29(12): 1065-1074.
HU Zhengping, XIE Ronglu, WANG Meng, SUN Zhe. Object Tracking via Global and Local Structural Inverse Sparse Appearance Model. , 2016, 29(12): 1065-1074.
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