A Topology-Aware Spatiotemporal Handover Framework for Continuous Multi-UAV Tracking
Summary
This paper presents a real-time multi-camera multi-vehicle tracking system for UAV-based traffic monitoring that uses a topology-based spatiotemporal handover mechanism and deterministic queue-based matching to maintain vehicle identity across camera views, achieving 99.8% handover success rate.
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Paper page - A Topology-Aware Spatiotemporal Handover Framework for Continuous Multi-UAV Tracking
Source: https://huggingface.co/papers/2605.15779
Abstract
A real-time multi-camera multi-vehicle tracking system addresses trajectory fragmentation in UAV-based traffic monitoring through a topology-based spatiotemporal handover mechanism and deterministic queue-based matching algorithm.
The integration ofUnmanned Aerial Vehicles(UAVs) intoIntelligent Transportation Systems(ITS) offers synoptic visibility for traffic monitoring, yet scalable deployment is hindered bytrajectory fragmentation, where vehicle identity persistence is lost across multi-UAVFields 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-basedRe-Identification(Re-ID) in nadir views, we introduce a lightweight Topology-Based Spatiotemporal Handover mechanism. We implement a high-throughput parallel pipeline leveragingYOLO11andByteTrackto process concurrent 4K streams. Our core contribution is a deterministic queue-based matching algorithm that utilizesgeometric overlapsandvirtual lane discretizationto predictively manage identity handover viaFIFO queues. Experimental results on complex urban environments, including intersections and merging traffic, demonstrate aHandover 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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