融合全局与局部信息，提升小目标无人机跟踪精度与连续性
GL-DT: Multi-UAV Detection and Tracking with Global-Local Integration
- 设计时空特征融合模块，联合建模运动与外观特征
- 在 UAVDT 与 VisDrone 评测中，IDF1 提升 6.2% 和 4.8%
- 适合需要高稳定性多无人机跟踪的军事与环境监测场景
无人飞行器在军事侦察、环境监测等领域的广泛应用，催生了对精准高效多目标跟踪（MOT）技术的迫切需求，这对无人机态势感知至关重要。然而，复杂背景、小尺度目标以及频繁遮挡和交互仍严重挑战现有方法在检测精度和轨迹连续性方面的表现。为此，本文提出全局-局部检测与跟踪（GL-DT）框架，采用时空特征融合（STFF）模块联合建模运动与外观特征，并结合全局-局部协同检测策略，有效提升小目标检测能力。在此基础上，引入JPTrack跟踪算法，缓解常见问题如身份切换（ID switch）和轨迹断裂。实验结果表明，该方法显著提升了MOT的连续性与稳定性，同时保持实时性能，为无人机检测与跟踪技术的发展提供有力支持。
原文摘要 · Abstract (English)
The extensive application of unmanned aerial vehicles (UAVs) in military reconnaissance, environmental monitoring, and related domains has created an urgent need for accurate and efficient multi-object tracking (MOT) technologies, which are also essential for UAV situational awareness. However, complex backgrounds, small-scale targets, and frequent occlusions and interactions continue to challenge existing methods in terms of detection accuracy and trajectory continuity. To address these issues, this paper proposes the Global-Local Detection and Tracking (GL-DT) framework. It employs a Spatio-Temporal Feature Fusion (STFF) module to jointly model motion and appearance features, combined with a global-local collaborative detection strategy, effectively enhancing small-target detection. Building upon this, the JPTrack tracking algorithm is introduced to mitigate common issues such as ID switches and trajectory fragmentation. Experimental results demonstrate that the proposed approach significantly improves the continuity and stability of MOT while maintaining real-time performance, providing strong support for the advancement of UAV detection and tracking technologies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。