This paper introduces a novel deep learning framework that enhances vehicle re-identification (ReID) accuracy by integrating visual and temporal data. Vehicle ReID, which identifies target vehicles from large volumes of traffic data, is essential for continuous tracking in large-scale monitoring scenarios involving multiple unmanned aerial vehicles (UAVs). UAV-based monitoring, while offering a comprehensive bird’s-eye view (BEV), faces key challenges: loss of uniquely identifiable features and reliance on visual data, which struggles with vehicles of similar appearance. To overcome these issues, our approach incorporates traffic-oriented features based on shockwave theory to model predictable vehicle travel times. Methods have been tested with data from one of the largest drone experiments with 10 drones monitoring 20 intersections for one week in the city of Songdo in Seoul Area. Experimental results demonstrate a 36.8% improvement in ReID accuracy over traditional methods, highlighting the potential of UAV-based solutions for robust and scalable traffic monitoring.
Tak et al. (Sun,) studied this question.