Object Tracking Method Based on Sparse Optimization of Local Sensing
LIU Daqian① LIU Wanjun② FEI Bowen③
①(School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China) ②(School of Software, Liaoning Technical University, Huludao 125105,China) ③(School of Business and Management, Liaoning Technical University, Huludao 125105, China)
Abstract：The problem of tracking drift is produced easily by traditional sparse representation tracking methods in complex scene. To solve this problem, a novel tracking approach based on sparse optimization of local sensing is proposed. Firstly, the object area of the first frame is divided into non-overlapping uniform segmentation, and building the template set using global features and local features. Then, a local sensing correction method for constraining sparse optimization matching process is utilized to determine the optimal matching samples. Finally, a new method of occlusion decision is used to detect occlusion, and updating strategies are adopted according to different occlusion conditions, which makes the template sets more complete in the process of template update. The experiments compare with state-of-the-art tracking algorithms on 10 tracking test sequences of benchmark library. Experiment results indicate that the proposed method possesses characteristics of accurate tracking and strong adaptability in the conditions of partial occlusion, deformation, and complex background.
刘大千,刘万军,费博雯. 局部感知下的稀疏优化目标跟踪方法[J]. 电子与信息学报, 2018, 40(2): 272-281.
LIU Daqian, LIU Wanjun, FEI Bowen. Object Tracking Method Based on Sparse Optimization of Local Sensing. JEIT, 2018, 40(2): 272-281.
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