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International Conference on Neural Information Processing

ICONIP 2018: Neural Information Processing pp 488–500Cite as

Correlation Filter Tracking with Complementary Features

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11306))

Abstract

Although Correlation Filters (CF) tracking algorithms have inherent capability to tackle various challenging scenarios individually, none of them are robust enough to handle all the challenges simultaneously. For any online tracking based on Correlation Filters, feature is one of the most important factors due to its representation power of target appearance. In this paper, we proposed a new tracking framework by integrating the advantage of complementary features to achieve robust tracking performance. The key issue of this work lies in the fact that different features respond to different tracking challenges, which also applies to deep learning features and hand-craft features. Moreover, for the tracking speed balance, we train a light-weight deep CNN features by using end-to-end learning method, which has the same Parameter magnitude as the hand-crafted features. Experimental results on OTB-2013, OTB-2015 large benchmarks datasets show that the proposed tracker performs favorably against several state-of-the-art methods.

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Author information

Authors and Affiliations

  1. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China

    Wei Wang, Weiguang Li & Mingquan Shi

  2. University of Chinese Academy of Sciences, Beijing, China

    Wei Wang & Weiguang Li

Authors
  1. Wei Wang
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  2. Weiguang Li
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  3. Mingquan Shi
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Corresponding author

Correspondence to Mingquan Shi .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Wang, W., Li, W., Shi, M. (2018). Correlation Filter Tracking with Complementary Features. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_42

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  • DOI: https://doi.org/10.1007/978-3-030-04224-0_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04223-3

  • Online ISBN: 978-3-030-04224-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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