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Adaptive Learning Rate and Spatial Regularization Background Perception Filter for Visual Tracking

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Artificial Intelligence (CICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13069))

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Abstract

In recent years, the correlation filter (CF) has excellent accuracy and speed in the field of visual object tracking. Training samples for CF are usually generated by circular shifts. Although such training samples combined with Fourier transform can be effective in reducing computational effort. They also give rise to boundary effects. Spatial regularization can effectively suppress the boundary effect, but the learning rate are fixed. They cannot be adaptively adjusted to match environmental changes, and the background information is not suppressed. In this paper, we propose a new Correlation filter model, namely Adaptive Learning Rate and Spatial Regularization Background Perception Filter for Visual Tracking (SRAL). Firstly, the SRAL uses real background information as negative samples to train the filter model. Secondly, we use the Average Peak to Correlation Energy (APCE) and the response value error between the two frames to adjust the learning rate together. In addition, the introduction of the regular term destroys the closed solution of CF, and this problem can be effectively solved by the use of the alternating direction method of multipliers (ADMM). Extensive experimental evaluations on three large tracking benchmarks are performed, which demonstrate the good performance of the proposed method over some of the state-of-the-art trackers.

This work was supported by the National Natural Science Foundation of China [grant number U1813220] and the Natural Science Foundation of Xinjiang Uygur Autonomous Region [grant number 2019D01C02].

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Xinjiang University, Urumqi, China

    Kai Lv, Liang Yuan & L. He

  2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China

    Liang Yuan & Ran Huang

  3. School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China

    Jie Mei

Authors
  1. Kai Lv
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  2. Liang Yuan
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  3. L. He
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  4. Ran Huang
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  5. Jie Mei
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Corresponding author

Correspondence to Liang Yuan .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Yiran Chen

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. University of British Columbia, Vancouver, BC, Canada

    Jane Wang

  5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

  6. Xidian University, Xi’an, China

    Weisheng Dong

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Lv, K., Yuan, L., He, L., Huang, R., Mei, J. (2021). Adaptive Learning Rate and Spatial Regularization Background Perception Filter for Visual Tracking. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_57

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  • DOI: https://doi.org/10.1007/978-3-030-93046-2_57

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-93046-2

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