解决多无人机追踪中目标身份丢失问题，实现高精度连续追踪
A Topology-Aware Spatiotemporal Handover Framework for Continuous Multi-UAV Tracking
- 用几何重叠和虚拟车道离散化预测身份交接，避免依赖耗时的外观识别
- 在复杂城市路况下实现99.8%的身份交接成功率，远超传统方法的74.1%
- 适用于交通监控、智能交通系统等需要全局目标追踪的场景
将无人机集成到智能交通系统中可实现交通状况的全景监控，但轨迹碎片化问题导致跨多无人机视域时车辆身份难以持续追踪。现有先进框架虽能优化单机影像中的轨迹提取与稳定性，却常作为孤立数据孤岛，生成断裂轨迹，阻碍网络级分析（如起讫点估计）。本文提出一种实时多相机多车辆追踪（MCMT）系统，保障全局身份持续性。针对俯视视角下基于外观的重识别（Re-ID）带来的视觉模糊与计算开销问题，设计轻量级拓扑感知时空交接机制。采用并行处理管道，结合YOLO11与ByteTrack，支持并发4K视频流处理。核心贡献为基于确定性队列的匹配算法，利用几何重叠与虚拟车道离散化，通过先进先出（FIFO）队列预判身份交接。在包含交叉口与汇流区的复杂城市环境中实验表明，连续车流下身份交接成功率（HOSR）达99.8%，显著优于Re-ID基线（74.1%），验证了边缘部署可行性。源码已开源。
原文摘要 · Abstract (English)
The integration of Unmanned Aerial Vehicles(UAVs) into Intelligent Transportation Systems (ITS) offers synoptic visibility for traffic monitoring, yet scalable deployment is hindered by trajectory fragmentation, where vehicle identity persistence is lost across multi-UAV Fields of View (FOV). While state-of-the-art frameworks excel in optimizing local trajectory extraction and stability for single-drone imagery, they often function as isolated data silos that generate disjointed trajectories, thereby precluding network-level analysis such as Origin-Destination estimation. This paper presents a real-time Multi-Camera Multi-Vehicle Tracking (MCMT) system designed to handle global identity persistence. Addressing the visual ambiguity and computational cost of appearance-based Re-Identification (Re-ID) in nadir views, we introduce a lightweight Topology-Based Spatiotemporal Handover mechanism. We implement a high-throughput parallel pipeline leveraging YOLO11 and ByteTrack to process concurrent 4K streams. Our core contribution is a deterministic queue-based matching algorithm that utilizes geometric overlaps and virtual lane discretization to predictively manage identity handover via FIFO queues. Experimental results on complex urban environments, including intersections and merging traffic, demonstrate a Handover Success Rate (HOSR) of 99.8% in continuous traffic flows, significantly outperforming Re-ID baselines (74.1%) while validating edge deployment feasibility. The source code is available at https://github.com/JYe9/multi-camera-multi-vehicle-tracking-system.
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