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Deep Relative Tracking
Gao, Junyu1,2; Zhang, Tianzhu1,2; Yang, Xiaoshan1,2; Xu, Changsheng1,2; Changsheng Xu
AbstractMost existing tracking methods are direct trackers, which directly exploit foreground or/and background information for object appearance modeling and decide whether an image patch is target object or not. As a result, these trackers cannot perform well when target appearance changes heavily and becomes different from its model. To deal with this issue, we propose a novel relative tracker, which can effectively exploit the relative relationship among image patches from both foreground and background for object appearance modeling. Different from direct trackers, the proposed relative tracker is robust to localize target object by use of the best image patch with the highest relative score to the target appearance model. To model relative relationship among large-scale image patch pairs, we propose a novel and effective deep relative learning algorithm through the convolutional neural network. We test the proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our method consistently outperforms the state-of-theart trackers due to the powerful capacity of the proposed deep relative model.
KeywordVisual Tracking Deep Learning Relative Model
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61225009 ; Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(IDHT20140224) ; 61432019 ; 61572498 ; 61532009 ; 61572296)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000398976000005
Citation statistics
Cited Times:51[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorChangsheng Xu
Affiliation1.National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Gao, Junyu,Zhang, Tianzhu,Yang, Xiaoshan,et al. Deep Relative Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(4):1845-1858.
APA Gao, Junyu,Zhang, Tianzhu,Yang, Xiaoshan,Xu, Changsheng,&Changsheng Xu.(2017).Deep Relative Tracking.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(4),1845-1858.
MLA Gao, Junyu,et al."Deep Relative Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.4(2017):1845-1858.
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