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Deep learning for vehicle ReID in urban traffic monitoring with visual and temporal information

Yura Tak et al.

Communications in Transportation Research2026https://doi.org/10.26599/commtr.2026.9640005article
ABDC C
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.26599/commtr.2026.9640005

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@article{yura2026,
  title        = {{Deep learning for vehicle ReID in urban traffic monitoring with visual and temporal information}},
  author       = {Yura Tak et al.},
  journal      = {Communications in Transportation Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.26599/commtr.2026.9640005},
}

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Deep learning for vehicle ReID in urban traffic monitoring with visual and temporal information

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.