Layer-Guided UAV Tracking: Enhancing Efficiency and Occlusion Robustness

• URL: http://arxiv.org/abs/2602.13636v1
• Date: Sat, 14 Feb 2026 07:02:25 GMT
• Title: Layer-Guided UAV Tracking: Enhancing Efficiency and Occlusion Robustness
• Authors: Yang Zhou, Derui Ding, Ran Sun, Ying Sun, Haohua Zhang,
• Abstract summary: LGTrack is a unified UAV tracking framework that integrates dynamic layer selection, efficient feature enhancement, and robust representation learning.<n> Experiments on three datasets demonstrate LGTrack's state-of-the-art real-time speed (258.7 FPS on UAVDT)
• Score: 12.719243469290346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual object tracking (VOT) plays a pivotal role in unmanned aerial vehicle (UAV) applications. Addressing the trade-off between accuracy and efficiency, especially under challenging conditions like unpredictable occlusion, remains a significant challenge. This paper introduces LGTrack, a unified UAV tracking framework that integrates dynamic layer selection, efficient feature enhancement, and robust representation learning for occlusions. By employing a novel lightweight Global-Grouped Coordinate Attention (GGCA) module, LGTrack captures long-range dependencies and global contexts, enhancing feature discriminability with minimal computational overhead. Additionally, a lightweight Similarity-Guided Layer Adaptation (SGLA) module replaces knowledge distillation, achieving an optimal balance between tracking precision and inference efficiency. Experiments on three datasets demonstrate LGTrack's state-of-the-art real-time speed (258.7 FPS on UAVDT) while maintaining competitive tracking accuracy (82.8\% precision). Code is available at https://github.com/XiaoMoc/LGTrack

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