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Robust visual tracking of infrared object via sparse representation model
Ma JK(马俊凯); Luo HB(罗海波); Chang Z(常铮); Hui B(惠斌)
Department光电信息技术研究室
Conference NameInternational Symposium on Optoelectronic Technology and Application 2014
Conference DateMay 13-15, 2014
Conference PlaceBeijing, China
Source PublicationProc. Of SPIE 9301, International Symposium on Optoelectronic Technology and Application
PublisherSPIE
Publication PlaceBellingham, WA
2014
Pages1-6
Indexed ByEI ; CPCI(ISTP)
EI Accession number20150800551816
WOS IDWOS:000349327100100
Contribution Rank1
ISSN0277-786X
KeywordSparse Representation Target Tracking Appearance Model Robust Tracking Particle Lter
Abstract

In this paper, we propose a robust tracking method for infrared object. We introduce the appearance model and the sparse representation in the framework of particle filter to achieve this goal. Representing every candidate image patch as a linear combination of bases in the subspace which is spanned by the target templates is the mechanism behind this method. The natural property, that if the candidate image patch is the target so the coefficient vector must be sparse, can ensure our algorithm successfully. Firstly, the target must be indicated manually in the first frame of the video, then construct the dictionary using the appearance model of the target templates. Secondly, the candidate image patches are selected in following frames and the sparse coefficient vectors of them are calculated via `1-norm minimization algorithm. According to the sparse coefficient vectors the right candidates is determined as the target. Finally, the target templates update dynamically to cope with appearance change in the tracking process. This paper also addresses the problem of scale changing and the rotation of the target occurring in tracking. Theoretic analysis and experimental results show that the proposed algorithm is elective and robust.

Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/15334
Collection光电信息技术研究室
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China
4.Key Lab of Image Understanding and Computer Vision, Liaoning province, Shenyang, China
Recommended Citation
GB/T 7714
Ma JK,Luo HB,Chang Z,et al. Robust visual tracking of infrared object via sparse representation model[C]. Bellingham, WA:SPIE,2014:1-6.
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