基于自适应模板更新的Transformer无人机目标跟踪算法
收稿日期: 2024-12-19
修回日期: 2025-02-13
录用日期: 2025-04-11
网络出版日期: 2025-04-25
基金资助
国家自然科学基金(61171119)
Adaptive template update-based Transformer algorithm for UAV target tracking
Received date: 2024-12-19
Revised date: 2025-02-13
Accepted date: 2025-04-11
Online published: 2025-04-25
Supported by
National Natural Science Foundation of China(61171119)
无人机已被广泛应用在军事和民用领域，目标跟踪是无人机应用的关键技术之一。针对无人机目标跟踪中目标易发生形变、遮挡、尺度变化以及环境复杂等问题，提出一种基于自适应模板更新的Transformer无人机目标跟踪算法。首先，基于改进非对称注意力机制构建Transformer骨干网络，有效提取了图像特征并增强了特征对目标的表达能力。其次，提出一种基于外观变化系数的自适应模板更新策略，通过计算外观变化系数，自适应进行模板更新，提升了跟踪网络处理目标变化的能力。最后，根据搜索区域图像特征响应图计算最大置信分数，确定目标位置。仿真实验结果表明，所提算法能够有效提升无人机目标跟踪的精度，具有较好的鲁棒性。
刘芳 , 卢晨阳 , 路言 , 王鑫 . 基于自适应模板更新的Transformer无人机目标跟踪算法[J]. 航空学报, 2025 , 46(16) : 331687 -331687 . DOI: 10.7527/S1000-6893.2025.31687
Unmanned Aerial Vehicles （UAVs） have been extensively deployed in both military and civilian applications， where target tracking plays a critical role. To address challenges such as target deformation， occlusion， scale variation， and complex environmental conditions during UAV target tracking， a adaptive template update-based Transformer algorithm for UAV target tracking is proposed. Specifically， a Transformer backbone network is constructed using an improved asymmetric attention mechanism to effectively extract image features and enhance the representation of target-related information. Furthermore， an adaptive template updating strategy based on an appearance variation coefficient is introduced. By dynamically computing this coefficient， the template is updated adaptively to improve the ability of network to cope with appearance changes of the target. Finally， the target position is determined by calculating the maximum confidence score from the response map of the search region. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of UAV target tracking and exhibits strong robustness.
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