TAE：面向夜间无人机跟踪的目标感知增强器
TAE: Target-aware enhancer for nighttime UAV tracking
AI总结：
提出一种目标感知的低光增强框架TAE，利用跟踪框弱监督信号进行区域感知增强和自适应RGB多曲线融合，显著提升夜间无人机跟踪性能，并贡献了包含268个序列的DarkSOT基准。
摘要：
夜间低光条件下的严重图像退化是基于无人机的单目标跟踪全天候应用的核心瓶颈。现有的图像增强方法通常难以区分目标和背景区域，容易放大背景噪声或损害目标特征。为克服这一限制，我们提出TAE，一种专为夜间目标跟踪设计的目标感知低光增强框架。在跟踪边界框的弱监督信号显式引导下，该框架进行区域感知增强，确保操作聚焦于目标区域。它进一步采用自适应RGB多曲线融合机制，实现不同区域的精细建模和自适应调整。为促进该领域研究，我们还贡献了DarkSOT，一个新的夜间无人机跟踪基准，包含9个目标类别的268个序列。在DarkSOT和UAVDark135上的实验结果表明，TAE显著提升了低光夜间场景下的跟踪性能，展现出强鲁棒性和泛化能力。DarkSOT数据集可在https://github.com/Fu0511/DarkSOT-Dataset获取。
英文摘要：
Severe image degradation under low-light nighttime conditions constitutes a core bottleneck preventing all-day applications for UAV-based single object tracking. Existing image enhancement methods often struggle to distinguish between target and background regions, which can easily lead to amplified background noise or compromise target features. To overcome this limitation, we propose TAE, a target-aware low-light enhancement framework tailored for nighttime object tracking. Guided explicitly by weak supervisory signals from tracking bounding boxes, the framework performs region-aware enhancement to ensure operations focus on the target area. It further adopts an adaptive RGB multi-curve fusion mechanism to achieve refined modeling and adaptive adjustment across different regions. To facilitate research in this domain, we also contribute DarkSOT, a new benchmark for nighttime UAV tracking, comprising 268 sequences across 9 target categories. Experimental results on the DarkSOT and UAVDark135 demonstrate that TAE significantly improves tracking performance in low-light nighttime scenarios, exhibiting strong robustness and generalization. The DarkSOT dataset is available at https://github.com/Fu0511/DarkSOT-Dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
