Urban Re-Identification: Fusing Local and Global Features with Residual Masked Maps for Enhanced Vehicle Monitoring in Small Datasets

William A. Ramirez, Cesar A. Sierra Franco, Thiago R. da Motta, Alberto Raposo

2025

Abstract

This paper presents an optimized vehicle re-identification (Re-ID) approach focused on small datasets. While most existing literature concentrates on deep learning techniques applied to large datasets, this work addresses the specific challenges of working with smaller datasets, mainly when dealing with incomplete partitioning information. Our approach explores automated regional proposal methods, examining residuality and uniform sampling techniques for connected regions through statistical methods. Additionally, we integrate global and local attributes based on mask extraction to improve the generalization of the learning process. This led to a more effective balance between small and large datasets, achieving up to an 8.3% improvement in Cumulative Matching Characteristics (CMC) at k=5 compared to attention-based methods for small datasets. We improved generalization regarding context changes of up to 13% in CMC for large datasets. The code, model, and DeepStream-based implementations are available at https://github.com/will9426/will9426-automatic-Regionproposal-for-cars-in-Re-id-models.

Paper Citation

in Harvard Style

Ramirez W., Franco C., R. da Motta T. and Raposo A. (2025). Urban Re-Identification: Fusing Local and Global Features with Residual Masked Maps for Enhanced Vehicle Monitoring in Small Datasets. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 574-581. DOI: 10.5220/0013176300003912

in Bibtex Style

@conference{visapp25,
author={William Ramirez and Cesar Franco and Thiago R. da Motta and Alberto Raposo},
title={Urban Re-Identification: Fusing Local and Global Features with Residual Masked Maps for Enhanced Vehicle Monitoring in Small Datasets},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={574-581},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013176300003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Urban Re-Identification: Fusing Local and Global Features with Residual Masked Maps for Enhanced Vehicle Monitoring in Small Datasets
SN - 978-989-758-728-3
AU - Ramirez W.
AU - Franco C.
AU - R. da Motta T.
AU - Raposo A.
PY - 2025
SP - 574
EP - 581
DO - 10.5220/0013176300003912
PB - SciTePress