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Dual Graph Regularized Discriminative Multitask Tracker
Fan BJ(范保杰)1; Cong Y(丛杨)2; Tang YD(唐延东)2
Department机器人学研究室
Source PublicationIEEE Transactions on Multimedia
ISSN1520-9210
2018
Volume20Issue:9Pages:2303-2315
Indexed BySCI ; EI
EI Accession number20180704801535
WOS IDWOS:000442358200006
Contribution Rank2
Funding OrganizationChina Postdoctoral Science Foundation ; Jiangsu Postdoctoral Science Foundation ; Foundation for the Talent of Nanjing University of Tele. and Com. ; National Nature Science Foundation ; Natural Science Foundation of Jiangsu Province
KeywordMulti-task Tracker Discriminative Low Rank Learning Geometric Structure Information Graph Regularization Collaborate Metric
Abstract

Multi-task and low rank learning methods have attracted increasing attention for visual tracking. However, most trackers only focus on learning appearance subspace basis or the sparse low rankness of representation, thus do not make full use of the structure information among and inside target candidates (or samples). In this work, we propose a dual graph regularized discriminative low rank learning for multi-task tracker, which integrates the discriminative subspace and intrinsic geometric structures among tasks. By constructing double graphs regula- tions from two views of multi-task observation, the developed modal not only exploits the intrinsic relationship among tasks, and preserves the spatial layout structure among the local patches inside each candidate, but also learns the salient features of the target samples. This operation is benefit to having good target representation and improving the performance of the tracker. Moreover, our developed tracker is a collaborate multi- task tracking model, and learns the discriminative subspace with adaptive dimension and optimal classifier simultaneously. Then, a collaborate metric is developed to find the best candidate, which integrates both classification reliability and representation accu- racy. Encouraging experimental results on a large set of public video sequences justify that our tracker performs favourably against many other state-of-the-art trackers.

Language英语
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS KeywordOBJECT TRACKING ; VISUAL TRACKING ; SPARSE REPRESENTATION ; LOW-RANK ; BENCHMARK
WOS Research AreaComputer Science ; Telecommunications
Funding ProjectChina Postdoctoral Science Foundation[2015M571785] ; China Postdoctoral Science Foundation[2016T90484] ; Jiangsu Postdoctoral Science Foundation[1402085C] ; Foundation for the Talent of Nanjing University of Tele. and Com.[NY215148] ; Foundation for the Talent of Nanjing University of Tele. and Com.[NY217061] ; National Nature Science Foundation[61722311] ; National Nature Science Foundation[U1613214] ; National Nature Science Foundation[51775284] ; Natural Science Foundation of Jiangsu Province[BK20151505]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21531
Collection机器人学研究室
Corresponding AuthorFan BJ(范保杰)
Affiliation1.Nanjing University of Posts and Telecommunications, 12577 Nanjing China 210003
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, shenyang China
Recommended Citation
GB/T 7714
Fan BJ,Cong Y,Tang YD. Dual Graph Regularized Discriminative Multitask Tracker[J]. IEEE Transactions on Multimedia,2018,20(9):2303-2315.
APA Fan BJ,Cong Y,&Tang YD.(2018).Dual Graph Regularized Discriminative Multitask Tracker.IEEE Transactions on Multimedia,20(9),2303-2315.
MLA Fan BJ,et al."Dual Graph Regularized Discriminative Multitask Tracker".IEEE Transactions on Multimedia 20.9(2018):2303-2315.
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