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A Two-Stage Method for Aerial Tracking in Adverse Weather Conditions

Author

Listed:
  • Yuan Feng

    (College of Science, Zhejiang University of Technology, Hangzhou 310023, China)

  • Xinnan Xu

    (College of Science, Zhejiang University of Technology, Hangzhou 310023, China)

  • Nuoyi Chen

    (College of Science, Zhejiang University of Technology, Hangzhou 310023, China)

  • Quanjian Song

    (College of Science, Zhejiang University of Technology, Hangzhou 310023, China)

  • Lufang Zhang

    (School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China)

Abstract

To tackle the issue of aerial tracking failure in adverse weather conditions, we developed an innovative two-stage tracking method, which incorporates a lightweight image restoring model DADNet and an excellent pretrained tracker. Our method begins by restoring the degraded image, which yields a refined intermediate result. Then, the tracker capitalizes on this intermediate result to produce precise tracking bounding boxes. To expand the UAV123 dataset to various weather scenarios, we estimated the depth of the images in the dataset. Our method was tested on two famous trackers, and the experimental results highlighted the superiority of our method. The comparison experiment’s results also validated the dehazing effectiveness of our restoration model. Additionally, the components of our dehazing module were proven efficient through ablation studies.

Suggested Citation

  • Yuan Feng & Xinnan Xu & Nuoyi Chen & Quanjian Song & Lufang Zhang, 2024. "A Two-Stage Method for Aerial Tracking in Adverse Weather Conditions," Mathematics, MDPI, vol. 12(8), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1216-:d:1377974
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    References listed on IDEAS

    as
    1. Da Li & Haoxiang Chai & Qin Wei & Yao Zhang & Yunhan Xiao, 2023. "PACR: Pixel Attention in Classification and Regression for Visual Object Tracking," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
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