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Online structured sparse learning with labeled information for robust object tracking
Fan BJ(范保杰); Cong Y(丛杨); Tang YD(唐延东)
作者部门机器人学研究室
关键词Robust Object Tracking Online Dictionary Learning And Updating Robust Sparse Coding Prior Information Joint Decision Metric
发表期刊Journal of Electronic Imaging
ISSN1017-9909
2017
卷号26期号:1页码:1-16
收录类别SCI ; EI
EI收录号20170403276301
WOS记录号WOS:000397059800037
产权排序2
资助机构China Postdoctoral Science Foundation (No. 2015M571785, 2016T90484), NSFC (U1613214, 61673254, 61533015) Jiangsu Postdoctoral Science Foundation (No. 1402085C), State Key Laboratory of Robotics Open Project and the Foundation for the Talent of Nanjing University of Tele. and Com. (No. NY215148), project supported by the open fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education (No. MCCSE2015A05)
摘要We formulate object tracking under the particle filter framework as a collaborative tracking problem. The priori information from training data is exploited effectively to online learn a discriminative and reconstructive dictionary, simultaneously without losing structural information. Specifically, the class label and the semantic structure information are incorporated into the dictionary learning process as the classification error term and ideal coding regularization term, respectively. Combined with the traditional reconstruction error, a unified dictionary learning framework for robust object tracking is constructed. By minimizing the unified objective function with different mixed norm constraints on sparse coefficients, two robust optimizing methods are developed to learn the high-quality dictionary and optimal classifier simultaneously. The best candidate is selected by minimizing the reconstructive error and classification error jointly. As the tracking continues, the proposed algorithms alternate between the robust sparse coding and the dictionary updating. The proposed trackers are empirically compared with 14 state-of-the-art trackers on some challenging video sequences. Both quantitative and qualitative comparisons demonstrate that the proposed algorithms perform well in terms of accuracy and robustness.
语种英语
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被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/19920
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, Automation College, No. 9, Wenyuan Road, Nanjing, 210023, China
2.Chinese Academy of Sciences, State Key Laboratory of Robotics, No. 114, Nanta street, Shenyang, 110016, China
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GB/T 7714
Fan BJ,Cong Y,Tang YD. Online structured sparse learning with labeled information for robust object tracking[J]. Journal of Electronic Imaging,2017,26(1):1-16.
APA Fan BJ,Cong Y,&Tang YD.(2017).Online structured sparse learning with labeled information for robust object tracking.Journal of Electronic Imaging,26(1),1-16.
MLA Fan BJ,et al."Online structured sparse learning with labeled information for robust object tracking".Journal of Electronic Imaging 26.1(2017):1-16.
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