针对夜间无人机追踪难题，提出高效多尺度自适应跟踪系统。
MATrack: Efficient Multiscale Adaptive Tracker for Real-Time Nighttime UAV Operations
- 设计多尺度特征融合模块，提升动态目标与静态模板的一致性。
- 在UAVDark135上精度、归一化精度和AUC分别领先SOTA 5.9%、5.4%、4.2%。
- 实测81帧/秒，适合夜间搜救、边境巡逻等实时机器人应用。
夜间无人机追踪面临真实机器人任务中的重大挑战。低光照不仅限制视觉感知能力，杂乱背景与频繁视角变化也导致现有追踪器易漂移或失效。尽管已有研究基于低光照增强与领域自适应提出解决方案，但实际系统中仍存在明显缺陷：低光照增强常引入视觉伪影，领域自适应计算开销大，现有轻量设计难以充分挖掘动态目标信息。针对这些关键问题，本文提出MATrack——专为夜间无人机追踪设计的多尺度自适应系统。该系统通过三个核心模块协同工作解决主要技术难点：多尺度层次融合（MHB）增强静态与动态模板间特征一致性；自适应关键令牌门（AKTG）精准识别复杂背景中的目标信息；夜间模板校准器（NTC）保障长时间序列下的稳定追踪性能。大量实验表明，MATrack在UAVDark135基准上，精度、归一化精度与AUC分别超越当前最优方法5.9%、5.4%和4.2%，同时保持81帧/秒的实时处理速度。在真实无人机平台上的测试进一步验证了系统的可靠性，证明其可为夜间搜救、边境巡逻等关键机器人应用提供稳定有效的追踪支持。
原文摘要 · Abstract (English)
Nighttime UAV tracking faces significant challenges in real-world robotics operations. Low-light conditions not only limit visual perception capabilities, but cluttered backgrounds and frequent viewpoint changes also cause existing trackers to drift or fail during deployment. To address these difficulties, researchers have proposed solutions based on low-light enhancement and domain adaptation. However, these methods still have notable shortcomings in actual UAV systems: low-light enhancement often introduces visual artifacts, domain adaptation methods are computationally expensive and existing lightweight designs struggle to fully leverage dynamic object information. Based on an in-depth analysis of these key issues, we propose MATrack-a multiscale adaptive system designed specifically for nighttime UAV tracking. MATrack tackles the main technical challenges of nighttime tracking through the collaborative work of three core modules: Multiscale Hierarchy Blende (MHB) enhances feature consistency between static and dynamic templates. Adaptive Key Token Gate accurately identifies object information within complex backgrounds. Nighttime Template Calibrator (NTC) ensures stable tracking performance over long sequences. Extensive experiments show that MATrack achieves a significant performance improvement. On the UAVDark135 benchmark, its precision, normalized precision and AUC surpass state-of-the-art (SOTA) methods by 5.9%, 5.4% and 4.2% respectively, while maintaining a real-time processing speed of 81 FPS. Further tests on a real-world UAV platform validate the system's reliability, demonstrating that MATrack can provide stable and effective nighttime UAV tracking support for critical robotics applications such as nighttime search and rescue and border patrol.
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