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Reliable Multi-Kernel Subtask Graph Correlation Tracker
Fan BJ(范保杰)1; Cong Y(丛杨)2; Tian JD(田建东)2; Tang YD(唐延东)2
Indexed BySCI ; EI
EI Accession number20203709158710
WOS IDWOS:000557351000002
Contribution Rank2
Funding OrganizationMinistry of Science and Technology of the People's Republic of ChinaMinistry of Science and Technology, China [2019YFB1310300] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61876092] ; State key Laboratory of Robotics [2019-O07] ; State Key Laboratory of Integrated Service Network [ISN20-08]
KeywordTarget tracking Correlation Kernel Feature extraction Robustness Laplace equations Layered multi-subtask learning correction filter tracking temporal-spatial consistency object tracking

Many astonishing correlation filter trackers pay limited concentration on the tracking reliability and locating accuracy. To solve the issues, we propose a reliable and accurate cross correlation particle filter tracker via graph regularized multi-kernel multi-subtask learning. Specifically, multiple non-linear kernels are assigned to multi-channel features with reliable feature selection. Each kernel space corresponds to one type of reliable and discriminative features. Then, we define the trace of each target subregion with one feature as a single view, and their multi-view cooperations and interdependencies are exploited to jointly learn multi-kernel subtask cross correlation particle filters, and make them complement and boost each other. The learned filters consist of two complementary parts: weighted combination of base kernels and reliable integration of base filters. The former is associated to feature reliability with importance map, and the weighted information reflects different tracking contribution to accurate location. The second part is to find the reliable target subtasks via the response map, to exclude the distractive subtasks or backgrounds. Besides, the proposed tracker constructs the Laplacian graph regularization via cross similarity of different subtasks, which not only exploits the intrinsic structure among subtasks, and preserves their spatial layout structure, but also maintains the temporal-spatial consistency of subtasks. Comprehensive experiments on five datasets demonstrate its remarkable and competitive performance against state-of-the-art methods.

WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS Research AreaComputer Science ; Engineering
Funding ProjectMinistry of Science and Technology of the People's Republic of China[2019YFB1310300] ; National Natural Science Foundation of China[61876092] ; State key Laboratory of Robotics[2019-O07] ; State Key Laboratory of Integrated Service Network[ISN20-08]
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Document Type期刊论文
Corresponding AuthorFan BJ(范保杰)
Affiliation1.Automation College, NJUPT, Nanjing 210049, China
2.Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016, China
Recommended Citation
GB/T 7714
Fan BJ,Cong Y,Tian JD,et al. Reliable Multi-Kernel Subtask Graph Correlation Tracker[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:8120-8133.
APA Fan BJ,Cong Y,Tian JD,&Tang YD.(2020).Reliable Multi-Kernel Subtask Graph Correlation Tracker.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,8120-8133.
MLA Fan BJ,et al."Reliable Multi-Kernel Subtask Graph Correlation Tracker".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):8120-8133.
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