A Topology-Aware Spatiotemporal Handover Framework for Continuous Multi-UAV Tracking

TL;DR

This paper introduces a real-time, topology-aware multi-UAV tracking system that maintains vehicle identities across multiple cameras using a lightweight handover mechanism, outperforming traditional Re-ID methods.

Contribution

The authors develop a deterministic, queue-based handover algorithm leveraging geometric cues for continuous multi-UAV vehicle tracking, with high success rates and real-time performance.

Findings

Handover Success Rate (HOSR) of 99.8% in complex urban scenarios

Outperforms Re-ID baselines with 74.1% success rate

Demonstrates feasibility of edge deployment for real-time traffic monitoring

Abstract

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.