基于分层极限学习机和局部稀疏模型的视觉跟踪算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201704002

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Abstract To address problems of appearance change and partial occlusion in target tracking, a tracking algorithm is presented via combing hierarchical extreme learning machine(HELM) and adaptive structural local sparse appearance model(ASLSAM). HELM is capable of extracting robust features and fast classification. ASLSAM can improve the tracking accuracy and handle the partial occlusion. Finally, results of both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the tracking process of the proposed algorithm is stable with high tacking precision.

Key words： Visual Tracking Hierarchical Extreme Learning Machine(HELM) Local Sparse Appearance Model(LSAM) Deformation Partial Occlusion

Received: 22 August 2016

Fund:Supported by National Natural Science Foundation of China(No.61471154)

About author:: (SUN Rui, born in 1976, Ph.D., professor. His research interests include computer vision and machine learning.)
(ZHANG Dongdong(Corresponding author), born in 1992, master student. His research interests include computer vision and target tracking.)
(GAO Jun, born in 1963, Ph.D., profe-
ssor. His research interests include intelligent information processing and pattern recognition.)

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	SUN Rui
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Cite this article:

SUN Rui,ZHANG Dongdong,GAO Jun. Visual Tracking via Hierarchical Extreme Learning Machine and Local Sparse Model[J]. , 2017, 30(4): 302-313.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201704002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I4/302

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