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Second-Order Spatial-Temporal Correlation Filters for Visual Tracking

Author

Listed:
  • Yufeng Yu

    (Department of Computer and Information Science, University of Macau, Macau 999078, China)

  • Long Chen

    (Department of Computer and Information Science, University of Macau, Macau 999078, China)

  • Haoyang He

    (Department of Statistics, Guangzhou University, Guangzhou 510006, China)

  • Jianhui Liu

    (Jiangsu Province Key Lab on Image Processing and Image Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

  • Weipeng Zhang

    (PLA Strategic Support Force, Beijing 450001, China)

  • Guoxia Xu

    (Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjovik, Norway)

Abstract

Discriminative correlation filters (DCFs) have been widely used in visual object tracking, but often suffer from two problems: the boundary effect and temporal filtering degradation. To deal with these issues, many DCF-based variants have been proposed and have improved the accuracy of visual object tracking. However, these trackers only adopt first-order data-fitting information and have difficulty maintaining robust tracking in unconstrained scenarios, especially in the case of complex appearance variations. In this paper, by introducing a second-order data-fitting term to the DCF, we propose a second-order spatial–temporal correlation filter (SSCF) learning model. To be specific, the SSCF tracker both incorporates the first-order and second-order data-fitting terms into the DCF framework and makes the learned correlation filter more discriminative. Meanwhile, the spatial–temporal regularization was integrated to develop a robust model in tracking with complex appearance variations. Extensive experiments were conducted on the benchmarking databases CVPR2013, OTB100, DTB70, UAV123, and UAVDT-M. The results demonstrated that our SSCF can achieve competitive performance compared to the state-of-the-art trackers. When penalty parameter λ was set to 10 − 5 , our SSCF gained DP scores of 0.882, 0.868, 0.706, 0.676, and 0.928 on the CVPR2013, OTB100, DTB70, UAV123, and UAVDT-M databases, respectively.

Suggested Citation

  • Yufeng Yu & Long Chen & Haoyang He & Jianhui Liu & Weipeng Zhang & Guoxia Xu, 2022. "Second-Order Spatial-Temporal Correlation Filters for Visual Tracking," Mathematics, MDPI, vol. 10(5), pages 1-15, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:684-:d:756011
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    References listed on IDEAS

    as
    1. Haoran Yang & Juanjuan Wang & Yi Miao & Yulu Yang & Zengshun Zhao & Zhigang Wang & Qian Sun & Dapeng Oliver Wu, 2019. "Combining Spatio-Temporal Context and Kalman Filtering for Visual Tracking," Mathematics, MDPI, vol. 7(11), pages 1-14, November.
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    Cited by:

    1. Liqiang Liu & Tiantian Feng & Yanfang Fu & Chao Shen & Zhijuan Hu & Maoyuan Qin & Xiaojun Bai & Shifeng Zhao, 2022. "Learning Adaptive Spatial Regularization and Temporal-Aware Correlation Filters for Visual Object Tracking," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    2. Weihua Ou & Jianping Gou & Shaoning Zeng & Lan Du, 2023. "Preface to the Special Issue “Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics”—Special Issue Book," Mathematics, MDPI, vol. 11(4), pages 1-4, February.

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