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A robust tracking method with adaptive local spatial sparse representation
Author(s) -
Zhang Qing,
Zhu Yuesheng,
Wu Songtao,
Luo Guibo,
Zhang Liming
Publication year - 2016
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.3555
Subject(s) - sparse approximation , linear subspace , artificial intelligence , representation (politics) , frame (networking) , tracking (education) , computer vision , eye tracking , pattern recognition (psychology) , process (computing) , construct (python library) , computer science , active appearance model , mathematics , template , robustness (evolution) , image (mathematics) , biochemistry , chemistry , gene , psychology , telecommunications , pedagogy , geometry , politics , political science , law , programming language , operating system
In this paper, a robust visual tracking method is proposed based on local spatial sparse representation. In the proposed approach, the learned target template is sparsely and compactly expressed by forming local spatial and trivial samples dynamically. An adaptive multiple subspaces appearance model is developed to describe the target appearance and construct the candidate target templates during the tracking process. An effective selection strategy is then employed to select the optimal sparse solution and locate the target accurately in the next frame. The experimental results have demonstrated that our method can perform well in the complex and noisy visual environment, such as heavy occlusions, dramatic illumination changes, and large pose variations in the video. Copyright © 2015 John Wiley & Sons, Ltd.