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Authors: William A. Ramirez ; Cesar A. Sierra Franco ; Thiago R. da Motta and Alberto Raposo

Affiliation: Pontifical Catholic University of Rio de Janeiro, Brazil

Keyword(s): Urban Re-Identification, Dilated Region Proposal, Local and Global Attribute Fusion, Residual Connection Modules, Multi-Object Tracking.

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 implementat ions are available at https://github.com/will9426/will9426-automatic-Regionproposal-for-cars-in-Re-id-models. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Ramirez, W. A., Franco, C. A. S., 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; ISSN 2184-4321, SciTePress, pages 574-581. DOI: 10.5220/0013176300003912

@conference{visapp25,
author={William A. Ramirez and Cesar A. Sierra 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},
issn={2184-4321},
}

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
IS - 2184-4321
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

- Science and Technology Publications, Lda.
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