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Layer-Guided UAV Tracking: Enhancing Efficiency and Occlusion Robustness

2026-02-14 07:02:25
Yang Zhou, Derui Ding, Ran Sun, Ying Sun, Haohua Zhang
arXiv_CV
               
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 this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2602.13636

PDF

https://arxiv.org/pdf/2602.13636.pdf

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