Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking
KANG Bin①② CAO Wenwen③ YAN Jun③ ZHANG Suofei①
①(School of Internet of Things, Nanjing University Posts and Telecommunications, Nanjing 210003, China) ②(Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China) ③(School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)
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.
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KANG Bin, CAO Wenwen, YAN Jun, ZHANG Suofei. Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking. JEIT, 2018, 40(7): 1619-1626.
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