Visual tracking algorithm based on the sparse-regularized subspace

doi:10.3788/OPE.20182604.0989

CIOMP OpenIR

	Visual tracking algorithm based on the sparse-regularized subspace
	Chen, Dian-Bing; Zhu, Ming; Wang, Hui-Li; Yang, Hang
	2018
发表期刊	Guangxue Jingmi Gongcheng/Optics and Precision Engineering
ISSN	1004924X
卷号	26 期号:4 页码:989-997
摘要	Aiming at the problem that the subspace representation tracking algorithm cannot deal with the occlusion problem effectively and the sparse representation tracking algorithm cannot meet the real-time requirements of the tracking, this paper proposed a sparse-regularized subspace visual tracking algorithm. The algorithm combined the advantages of subspace representation and sparse representation, improved the processing ability of the occlusion problem, and reduced the computational complexity. Firstly, the algorithm adopted the PCA subspace basis, the subspace mean and the representation residual to represent the target, and used the L2norm as the regularization of the representation coefficient and the representation residual. Secondly, the algorithm applied an iteration method to compute the coefficients and the residual, then constructed different update templates according to different non-zero ratio of the residual which was preprocessed by opening operator, and employed the incremental principal component analysis method to learn new PCA subspace basis and PCA subspace mean. Through this way, the algorithm enforced the subspace basis and the subspace mean to describe the variation of the target continuously and accurately during tracking progress. Finally, experimental results on qualitative and quantitative aspects analysis showed that average center location error of the proposed algorithm was 5.3 pixels in all 10 experimental sequences, and average overlap rate was 77%. Compared with eight state-of-art algorithms, the proposed algorithm obtains a more precise result and has better robustness and can meet tracking requirements in more situations. 2018, Science Press. All right reserved.
关键词	Iterative methods Principal component analysis Tracking (position)
DOI	10.3788/OPE.20182604.0989
收录类别	EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/60853
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Chen, Dian-Bing,Zhu, Ming,Wang, Hui-Li,et al. Visual tracking algorithm based on the sparse-regularized subspace[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2018,26(4):989-997.
APA	Chen, Dian-Bing,Zhu, Ming,Wang, Hui-Li,&Yang, Hang.(2018).Visual tracking algorithm based on the sparse-regularized subspace.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,26(4),989-997.
MLA	Chen, Dian-Bing,et al."Visual tracking algorithm based on the sparse-regularized subspace".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 26.4(2018):989-997.

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