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Learning Multi-feature Based Spatially Regularized and Scale Adaptive Correlation Filters for Visual Tracking

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MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11961))

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Abstract

Visual object tracking is a challenging problem in computer vision. Although the correlation filter-based trackers have achieved competitive results both on accuracy and robustness in recent years, there is still a need to improve the overall tracking performance. In this paper, to tackle the problems caused by Spatially Regularized Discriminative Correlation Filter (SRDCF), we suggest a single-sample-integrated training scheme which utilizes information of the previous frames and the current frame to improve the robustness of training samples. Moreover, manually designed features and deep convolutional features are integrated together to further boost the overall tracking capacity. To optimize the translation filter, we develop an alternating direction method of multipliers (ADMM) algorithm. Besides, we introduce a scale adaptively filter to directly learn the appearance changes induced by variations in the target scale. Extensive empirical evaluations on the TB-50, TB-100 and OTB-2013 datasets demonstrate that the proposed tracker is very promising for various challenging scenarios.

Supported by National Natural Science Foundation of China (No. 61672546 and No. 61573385), and Guangzhou Science and Technology Project (No. 201707010127).

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Author information

Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Ying She & Yang Yi

  2. School of Information Science, Xinhua College of Sun Yat-sen University, Guangzhou, China

    Yang Yi

  3. Guangdong Province Key Laboratory of Big Data Analysis and Processing, Guangzhou, China

    Yang Yi

Authors
  1. Ying She
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  2. Yang Yi
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Corresponding author

Correspondence to Ying She .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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She, Y., Yi, Y. (2020). Learning Multi-feature Based Spatially Regularized and Scale Adaptive Correlation Filters for Visual Tracking. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_39

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  • DOI: https://doi.org/10.1007/978-3-030-37731-1_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37730-4

  • Online ISBN: 978-3-030-37731-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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