Affine hull based target representation for visual tracking |
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| Affiliation: | 1. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Technology, Xiamen University, Xiamen 361005, China;2. Cognitive Science Department, Xiamen University, Xiamen 361005, China;3. Computer Science Department, Xiamen University, Xiamen 361005, China;1. Key Lab of Intelligent Information Processing, Institute of Computing technology, Chinese Academy of Sciences, Beijing 100190, China;2. Institute of Digital Media, Peking University, Beijing 100871, China;3. School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen 518005, China;1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China;2. School of Electronic Engineering, Xidian University, Xi’an 710071, China;3. School of Computer Engineering, Nanyang Technological University, 639798, Singapore;4. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore;1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;1. No.38 Research Institute, China Electronic Technology Group Corporation, Hefei 230088, PR China;2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, PR China |
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| Abstract: | Handling appearance variations is a challenging issue in visual tracking. Existing appearance models are usually built upon a linear combination of templates. With such kind of representation, accurate visual tracking is not desirable when heavy appearance variations are in presence. Under the framework of particle filtering, we propose a novel target representation for tracking. Namely, the target candidates are represented by affine combinations of a template set, which leads to better capability in describing unseen target appearances. Additionally, in order to adapt this representation to dynamic contexts across a video sequence, a novel template update scheme is presented. Different from conventional approaches, the scheme considers both the importance of one template to a target candidate in the current frame and the recentness of the template that is kept in the template set. Comprehensive experiments show that the proposed algorithm achieves superior performances in comparison with state-of-the-art works. |
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| Keywords: | Visual tracking Particle filter Affine hull Histograms of sparse codes Appearance model Target representation Generative tracking Template set |
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