基于局部敏感核稀疏表示的视频跟踪

doi:10.11999/JEIT150785

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Abstract

In order to solve the problem of lack of discriminability in the l₁-norm constraint sparse representation, visual tracking via locality-sensitive kernel sparse representation is proposed. To improve the linear discriminable power, the candidates’ Scale-Invariant Feature Transform (SIFT) is mapped into high dimension kernel space using the Gaussian kernel function. The locality-sensitive kernel sparse representation is acquired in the kernel space. The candidates’ representation are obtained after multi-scale maximum pooling. Finally, the candidates’ representation is put into the classifier and the candidate with the biggest Support Vector Machines (SVMs) score is recognized as the target. And the experiments demonstrate that the robustness of the proposed algorithm is improved due to the use of the data locality under the kernel sparse representation.

Key words： Visual tracking Kernel sparse representation Locality-sensitive constraint Support Vector Machine (SVM)

Received: 29 June 2015 Published: 14 January 2016

PACS:

TP391

Fund:

The National Natural Science Foundation of China (61175029, 61379104, 61372167), The Young Scientists Fund of the National Natural Science Foundation of China (61203268, 61202339)

Corresponding Authors: HUANG Hongtu E-mail: huanghongtu@sina.cn

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	HUANG Hongtu
	BI Duyan
	GAO Shan
	ZHA Yufei
	HOU Zhiqiang

Cite this article:

HUANG Hongtu,BI Duyan,GAO Shan等. Visual Tracking via Locality-sensitive Kernel Sparse Representation[J]. JEIT, 2016, 38(4): 993-999.

URL:

http://jeit.ie.ac.cn/EN/ 10.11999/JEIT150785 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I4/993

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