一种用于连续多无人机跟踪的拓扑感知时空切换框架

摘要

将无人机（UAV）集成到智能交通系统（ITS）中，为交通监测提供了全景视野，但可扩展部署仍受限于轨迹碎片化问题——当车辆跨越多个无人机视场（FOV）时，其身份持续性会丢失。尽管现有框架在优化单机影像的局部轨迹提取与稳定性方面表现优异，但其运作模式往往如同孤立的数据孤岛，生成断裂的轨迹，从而阻碍了起讫点估计等网络级分析。本文提出一种实时多相机多车辆跟踪（MCMT）系统，旨在解决全局身份持续性问题。针对俯视视角下基于外观重识别（Re-ID）存在的视觉模糊性和计算成本问题，我们引入了一种轻量化的拓扑时空交接机制。通过采用YOLO11和ByteTrack构建高吞吐量并行流水线，我们实现了对同步4K视频流的处理。核心贡献在于提出一种基于队列的确定性匹配算法：利用几何重叠区域与虚拟车道离散化，通过FIFO队列实现身份交接的预测性管理。在城市复杂环境（包含交叉口与合流交通）的实验结果表明，该方案在连续交通流中实现了99.8%的交接成功率（HOSR），显著优于基于Re-ID的基线方法（74.1%），同时验证了边缘部署的可行性。源代码开源地址为：https://github.com/JYe9/multi-camera-multi-vehicle-tracking-system。

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.