Visual tracking based on the sparse representation of the PCA subspace

	Visual tracking based on the sparse representation of the PCA subspace
	Chen, D.-b.; M. Zhu and H.-l. Wang
	2017
发表期刊	Optoelectronics Letters
卷号	13 期号:5
摘要	We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis (PCA) subspace, and then we employ an L1regularization to restrict the sparsity of the residual term, an L2regularization term to restrict the sparsity of the representation coefficients, and an L2norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods. 2017, Tianjin University of Technology and Springer-Verlag GmbH Germany.
收录类别	sci ; ei
语种	英语
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/58846
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Chen, D.-b.,M. Zhu and H.-l. Wang. Visual tracking based on the sparse representation of the PCA subspace[J]. Optoelectronics Letters,2017,13(5).
APA	Chen, D.-b.,&M. Zhu and H.-l. Wang.(2017).Visual tracking based on the sparse representation of the PCA subspace.Optoelectronics Letters,13(5).
MLA	Chen, D.-b.,et al."Visual tracking based on the sparse representation of the PCA subspace".Optoelectronics Letters 13.5(2017).

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