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

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• 作者：Gun Li ; Zhong-yuan Liu ; Hou-biao Li ; Peng Ren
• 关键词：Target tracking ; Particle filtering ; Sparse representation ; Redundant dictionary
• 刊名：Cognitive Computation
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：8
• 期：5
• 页码：910-923
• 全文大小：28,971 KB
• 刊物主题：Neurosciences; Computation by Abstract Devices; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics;
• 出版者：Springer US
• ISSN：1866-9964
• 卷排序：8

文摘

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.