一種用於連續多無人機追蹤的拓撲感知時空交接框架

摘要

將無人飛行載具（UAV）整合至智慧型運輸系統（ITS），可為交通監控提供綜觀視野，然而其大規模部署受限於軌跡片段化問題——即車輛身分標記在多UAV視野（FOV）間無法持續留存。儘管現有先進框架在單一無人機影像的局部軌跡萃取與穩定性優化方面表現卓越，但其運作模式常如獨立資料孤島，產生不連續的軌跡，進而阻礙起訖點推估等網路層級分析。本文提出一套即時多攝影機多車輛追蹤（MCMT）系統，旨在處理全域身分持續性問題。為解決俯視視角中外觀型重新識別（Re-ID）的視覺模糊性與計算成本，我們引入輕量級的「基於拓撲的時空交接機制」。我們採用YOLO11與ByteTrack實現高通量平行管線，以處理並行4K視訊串流。核心貢獻在於一套基於確定性佇列的匹配演算法，該演算法利用幾何重疊與虛擬車道離散化，經由先進先出（FIFO）佇列預測性地管理身分交接。在包含交叉路口與合流車流等複雜城市環境的實驗結果顯示，連續車流中的交接成功率（HOSR）達99.8%，大幅超越以重新識別為基線的方法（74.1%），同時驗證了邊緣端部署的可行性。原始碼公開於 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.