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A novel multilinear predictor for fast visual tracking

  • Published:
Journal of Electronics (China)

Abstract

This letter presents a novel prediction scheme employed for fast visual tracking. The proposed multilinear predictor is formulated as a higher order tensor, instead of the existing vector representations. This predictor is based on emploing the Canonical/Parallel factors (CP) decomposition to decompose a tensor as a sum of rank one tensors. In that way, the proposed scheme efficiently retains the underlying structural information of the input data, while reduces at the same time the computational complexity by employing separable filter operations applied at different directions. The efficiency of the proposed scheme is demonstrated in the conducted experiments.

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Authors and Affiliations

  1. ATR Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, China

    Weiwei Guo

  2. School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai, 200433, China

    Wenxian Yu

Authors
  1. Weiwei Guo
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  2. Wenxian Yu
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Corresponding author

Correspondence to Weiwei Guo.

Additional information

Communication author: Guo Weiwei, born in 1983, male, Ph.D..

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Guo, W., Yu, W. A novel multilinear predictor for fast visual tracking. J. Electron.(China) 29, 158–165 (2012). https://doi.org/10.1007/s11767-012-0755-5

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  • DOI: https://doi.org/10.1007/s11767-012-0755-5

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