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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)
作者部门光电信息技术研究室
会议名称International Symposium on Optoelectronic Technology and Application 2014
会议日期May 13-15, 2014
会议地点Beijing, China
会议录名称Proc. Of SPIE 9301, International Symposium on Optoelectronic Technology and Application
出版者SPIE
出版地Bellingham, WA
2014
页码1-6
收录类别EI ; CPCI(ISTP)
EI收录号20150800551816
WOS记录号WOS:000349327100100
产权排序1
ISSN号0277-786X
关键词Sparse Representation Target Tracking Appearance Model Robust Tracking Particle Lter
摘要

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.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/15334
专题光电信息技术研究室
作者单位1.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
推荐引用方式
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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