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Communication Dans Un Congrès Année : 2024

GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions

Résumé

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.
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Dates et versions

hal-04524979 , version 1 (28-03-2024)

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Paternité - Pas de modifications

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Citer

Moncef Boujou, Rabah Iguernaissi, Lionel Nicod, Djamal Merad, Séverine Dubuisson. GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions. 19th International Conference on Computer Vision Theory and Applications, Feb 2024, Rome, Italy. pp.493-500, ⟨10.5220/0012364200003660⟩. ⟨hal-04524979⟩
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