Visual tracking with structured patch-based model |
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| Affiliation: | 1. School of Information and Communication Engineering, Dalian University of Technology, Dalian 116023, China;2. ESAT-PSI/VISICS, KU Leuven, Belgium;3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore;1. School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China;2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;3. Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration, Ministry of Industry and Information Technology, China;1. Authentic Vision GmbH, Austria;2. Department of Computer Sciences, University of Salzburg, Austria;1. Computing Department, Imperial College, London, UK;2. Department of Computer Science, Rutgers University, USA;1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou350116, China;2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China;3. Tan Kah Kee College, Xiamen University, Zhangzhou363105, China;1. Department of Automation, Tsinghua University, State Key Lab of Intelligent Technologies and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China;2. Institute for Infocomm, Research Agency for Science, Technology and Research (A*STAR), 138632, Singapore;3. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;4. School of Computer Science Software Engineering, The University of Western Australia, Crawley, WA6009, Australia |
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| Abstract: | In this paper, we present a novel structured patch-based visual tracking method, which models the appearance of individual patches and their structural relationships within a unified framework. Specifically, this framework is defined as an optimal patch selection task, and can be further formulated as a linear programming problem, tractable and efficient in tracking scenario. To account for the changing appearance of the target object during tracking process, a pyramid local covariance descriptor is proposed to fuse multiple image characteristics. We compare the proposed method with other competing trackers by the recent large-scale benchmark. Extensive experimental results demonstrate that our tracker performs favorably against the state-of-the-art tracking algorithms. |
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