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CCF Conference on Big Data

Big Data 2018: Big Data pp 210–226Cite as

Learning Noise-Aware Correlation Filter for Visual Tracking

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 945))

Abstract

Correlation filter has recently attracted much attention in visual tracking due to their excellent performance on both accuracy and efficiency. However, the adopted features, such as Colors, HOG and deep features, usually include noises and/or corruptions which might disturb the tracking performance. To handle this problem, we propose a novel noise-aware correlation filter method for robust visual tracking. In particular, we decompose the input feature matrix into a “clean” feature matrix and a sparse noise matrix, and then use the “clean” feature to train the correlation filter. To optimize the proposed correlation filter, we design an efficient ADMM (alternation direction of multipliers) solver. Extensive experimental results on the OTB-2013 dataset show that the proposed approach performs favorably against state-of-the-art trackers.

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Acknowledgment

This work is jointly supported by National Natural Science Foundation of China (61702002, 61472002), China Postdoctoral Science Foundation, Natural Science Foundation of Anhui Province (1808085QF187), Natural Science Foundation of Anhui Higher Education Institution of China (KJ2017A017), and Co-Innovation Center for Information Supply & Assurance Technology, Anhui University.

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Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, China

    Xinyan Liang, Xiao Wang, Jin Tang & Chenglong Li

  2. Center for Research on Intelligent Perception and Computing, NLPR, CASIA, Beijing, China

    Chenglong Li

Authors
  1. Xinyan Liang
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  2. Xiao Wang
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  3. Jin Tang
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  4. Chenglong Li
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Corresponding author

Correspondence to Chenglong Li .

Editor information

Editors and Affiliations

  1. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China

    Zongben Xu

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Xidian University, Xi'an, Shaanxi, China

    Qiguang Miao

  4. Chinese Academy of Sciences, Beijing, China

    Yunquan Zhang

  5. Zhejiang University, Hangzhou, China

    Jiajun Bu

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Cite this paper

Liang, X., Wang, X., Tang, J., Li, C. (2018). Learning Noise-Aware Correlation Filter for Visual Tracking. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_14

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  • DOI: https://doi.org/10.1007/978-981-13-2922-7_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2921-0

  • Online ISBN: 978-981-13-2922-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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