Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking

KANG Bin; CAO Wenwen; YAN Jun; ZHANG Suofei

doi:10.11999/JEIT170939

Volume 40 Issue 7

Jul. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(7): 1619-1626

KANG Bin, CAO Wenwen, YAN Jun, ZHANG Suofei. Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1619-1626. doi: 10.11999/JEIT170939

Citation:

KANG Bin, CAO Wenwen, YAN Jun, ZHANG Suofei. Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1619-1626. doi: 10.11999/JEIT170939

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Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking

doi: 10.11999/JEIT170939

KANG Bin^①② CAO Wenwen^③ YAN Jun^③ ZHANG Suofei^①

Funds:

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

Received Date: 2017-10-11
Rev Recd Date: 2018-03-14
Publish Date: 2018-07-19

Abstract

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.
- Visual tracking、Sparse representation、Canonical correlation analysis,

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References(24)

References

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