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<p>Correlation filters (CFs) manifest the advantage of high computational efficiency in target tracking, especially in unmanned aerial vehicles (UAVs) target tracking. CF-based trackers update their models whenever necessary to improve the discrimination of the models. However, most existing update strategies only use the single-frame information to evaluate the reliability of the current tracking result, while neglecting the temporal relationship between frames, which could lead to the model degradation problem. In this paper, an Efficient spatial-temporal UAV visual tracker with a Temporal Enhancement model Update strategy (ETEU) is proposed. Specifically, this model update strategy is established based on the temporal correlation of the learned filters and the fluctuation degree of the current frame response map, which updates the models only at high confidence levels and thus alleviates the model degradation problem. Meanwhile, in order to achieve robust tracking in both temporal and spatial domains, we introduce a temporal regularization term and extract background patches as samples for training. Extensive experiments on six aerial tracking datasets, i.e., UAVDT, DTB70, UAV123@10fps, UAVTrack112, UAV123, and VisDrone2018, demonstrate the effectiveness of the proposed tracker. Furthermore, we deploy the proposed tracker on quadrotor UAVs in real-world scenarios and successfully completed the UAV target tracking task. Our code and results are available at <a href="https://github.com/chenxlin222/ETEU">https://github.com/chenxlin222/ETEU</a>.</p>

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An efficient spatial-temporal UAV visual tracker with the temporal enhancement model update strategy

  • 期刊论文

  • 发表日期：

    2025年1月17日

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  • Lin Chen,
  • Yungang Liu,
  • Yuan Wang

摘要

Correlation filters (CFs) manifest the advantage of high computational efficiency in target tracking, especially in unmanned aerial vehicles (UAVs) target tracking. CF-based trackers update their models whenever necessary to improve the discrimination of the models. However, most existing update strategies only use the single-frame information to evaluate the reliability of the current tracking result, while neglecting the temporal relationship between frames, which could lead to the model degradation problem. In this paper, an Efficient spatial-temporal UAV visual tracker with a Temporal Enhancement model Update strategy (ETEU) is proposed. Specifically, this model update strategy is established based on the temporal correlation of the learned filters and the fluctuation degree of the current frame response map, which updates the models only at high confidence levels and thus alleviates the model degradation problem. Meanwhile, in order to achieve robust tracking in both temporal and spatial domains, we introduce a temporal regularization term and extract background patches as samples for training. Extensive experiments on six aerial tracking datasets, i.e., UAVDT, DTB70, UAV123@10fps, UAVTrack112, UAV123, and VisDrone2018, demonstrate the effectiveness of the proposed tracker. Furthermore, we deploy the proposed tracker on quadrotor UAVs in real-world scenarios and successfully completed the UAV target tracking task. Our code and results are available at https://github.com/chenxlin222/ETEU.