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Authors: Moncef Boujou 1 ; Rabah Iguernaissi 1 ; Lionel Nicod 2 ; Djamal Merad 1 and Séverine Dubuisson 1

Affiliations: 1 LIS, CNRS, Aix-Marseille University, Marseille, France ; 2 CERGAM, Aix-Marseille University, Marseille, France

Keyword(s): Deep Learning, Computer Vision, Person Re-Identification, Gait Recognition, Representation Learning.

Abstract: Video-based person re-identification (Re-ID) is a challenging task aiming to match individuals across various cameras based on video sequences. While most existing Re-ID techniques focus solely on appearance information, including gait information, could potentially improve person Re-ID systems. In this study, we propose, GAF-Net, a novel approach that integrates appearance with gait features for re-identifying individuals; the appearance features are extracted from RGB tracklets while the gait features are extracted from skeletal pose estimation. These features are then combined into a single feature allowing the re-identification of individuals. Our numerical experiments on the iLIDS-Vid dataset demonstrate the efficacy of skeletal gait features in enhancing the performance of person Re-ID systems. Moreover, by incorporating the state-of-the-art PiT network within the GAF-Net framework, we improve both rank-1 and rank-5 accuracy by 1 percentage point.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Boujou, M.; Iguernaissi, R.; Nicod, L.; Merad, D. and Dubuisson, S. (2024). GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 493-500. DOI: 10.5220/0012364200003660

@conference{visapp24,
author={Moncef Boujou. and Rabah Iguernaissi. and Lionel Nicod. and Djamal Merad. and Séverine Dubuisson.},
title={GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={493-500},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012364200003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions
SN - 978-989-758-679-8
IS - 2184-4321
AU - Boujou, M.
AU - Iguernaissi, R.
AU - Nicod, L.
AU - Merad, D.
AU - Dubuisson, S.
PY - 2024
SP - 493
EP - 500
DO - 10.5220/0012364200003660
PB - SciTePress