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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
作者部门机器人学研究室
关键词Multi-task Tracker Discriminative Low Rank Learning Geometric Structure Information Graph Regularization Collaborate Metric
发表期刊IEEE Transactions on Multimedia
ISSN1520-9210
2018
卷号20期号:9页码:2303-2315
收录类别SCI ; EI
EI收录号20180704801535
WOS记录号WOS:000442358200006
产权排序2
资助机构China 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
摘要

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.

语种英语
WOS类目Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
关键词[WOS]OBJECT TRACKING ; VISUAL TRACKING ; SPARSE REPRESENTATION ; LOW-RANK ; BENCHMARK
WOS研究方向Computer Science ; Telecommunications
资助项目China 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]
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/21531
专题机器人学研究室
通讯作者Fan BJ(范保杰)
作者单位1.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
推荐引用方式
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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