基于典型相关性分析的稀疏表示目标追踪

doi:10.11999/JEIT170939

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Abstract In traditional sparse representation based visual tracking, particle sampling is first achieved by particle filter method. Then the particle observations are represented by intensity feature. Finally, the visual tracking is achieved by the intensity feature based sparse representation model. Different from traditional sparse representation model, a canonical correlation analysis based sparse representation model is proposed in this paper. The proposed model first uses two kinds of features to represent the particle observations, then, the projections of particle observations are used to build the sparse representation model. The advantage of the proposed model lies in that it can give a proper multi-feature fusing through canonical correlation analysis, which explores the relation between two features in a latent common subspace.

Key words： Visual tracking Sparse representation Canonical correlation analysis

Received: 11 October 2017 Published: 10 April 2018

PACS:

TP391

Fund:The National Natural Science Foundation of China (61771256, 61471205, 61771258, 61701252), The Natural Science Foundation of Jiangsu Province (BK20170915), The Natural Science Foundation of Jiangsu Higher Education Institutions (17KJD510005), The Nanjing University of Posts and Telecommunications Program (NY 216023), Supported by Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications

Corresponding Authors: YAN Jun E-mail: yanj@njupt.edu.cn

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	KANG Bin
	CAO Wenwen
	YAN Jun
	ZHANG Suofei

Cite this article:

KANG Bin,CAO Wenwen,YAN Jun等. Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking[J]. JEIT, 2018, 40(7): 1619-1626.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170939 OR http://jeit.ie.ac.cn/EN/Y2018/V40/I7/1619

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