Robust visual tracking via CAMShift and structural local sparse appearance model |
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| Affiliation: | 1. CAS Key Laboratory of Organic Solids, Beijing National Laboratory for Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China;2. National Center for Nanoscience and Technology, Beijing 100190, China;1. College of Information Science and Engineering, Hunan University, Changsha 410082, China;2. Department of Computer Science, State University of New York, New Paltz, NY 12561, USA;1. The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, No. 38 Zheda Road, Hangzhou, Zhejiang 310027, PR China;2. The Institute of Spacecraft System Engineering, No. 104 Youyi Road, Haidian, Beijing 100094, PR China;1. Department of Mathematics, Tongji University, Shanghai, China;2. Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong;1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China;2. Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States;3. School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;1. Department of Neurosurgery, The Chinese PLA General Hospital, Beijing 100853, China;2. State Key Laboratory of Brain and Cognitive Science, Beijing MR Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | This paper addresses issues in visual tracking where videos contain object intersections, pose changes, occlusions, illumination changes, motion blur, and similar color distributed background. We apply the structural local sparse representation method to analyze the background region around the target. After that, we reduce the probability of prominent features in the background and add new information to the target model. In addition, a weighted search method is proposed to search the best candidate target region. To a certain extent, the weighted search method solves the local optimization problem. The proposed scheme, designed to track single human through complex scenarios from videos, has been tested on some video sequences. Several existing tracking methods are applied to the same videos and the corresponding results are compared. Experimental results show that the proposed tracking scheme demonstrates a very promising performance in terms of robustness to occlusions, appearance changes, and similar color distributed background. |
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| Keywords: | Visual tracking Sparse representation Background suppression Target model update CAMShift Similar color distributed background Illumination changes Motion blur |
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