轻量级无人机追踪框架，提升遮挡鲁棒性与运行效率
Layer-Guided UAV Tracking: Enhancing Efficiency and Occlusion Robustness
- 动态选择特征层，结合轻量注意力机制增强特征表达
- 实测达258.7帧/秒，精度82.8%，兼顾速度与准确率
- 适合实时无人机视觉追踪场景，尤其抗遮挡能力强
视觉目标跟踪在无人机应用中至关重要。针对准确率与效率之间的权衡，尤其是在不可预测遮挡等挑战性条件下，仍面临重大挑战。本文提出LGTrack，一种统一的无人机跟踪框架，融合动态层选择、高效特征增强和遮挡鲁棒表示学习。通过新颖的轻量级全局分组坐标注意力（GGCA）模块，捕获长程依赖与全局上下文，以极低计算开销提升特征判别力。此外，轻量级相似度引导层自适应（SGLA）模块替代知识蒸馏，在跟踪精度与推理效率间实现最优平衡。在三个数据集上的实验表明，LGTrack在UAVDT上达到258.7 FPS的最先进实时速度，同时保持82.8%的精度。代码已公开于https://github.com/XiaoMoc/LGTrack。
原文摘要 · Abstract (English)
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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