Papers › GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions
GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions
Moncef Boujou, Rabah Iguernaissi, Lionel Nicod, Djamal Merad, Séverine Dubuisson
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Person Re-Identification | iLIDS-VID | GAF-Net | Rank-1 | 93.07 | #1 of 10 | Archive leaderboard | report |
| Person Re-Identification | iLIDS-VID | GAF-Net | Rank-10 | 99.74 | #1 of 10 | Archive leaderboard | report |
| Person Re-Identification | iLIDS-VID | GAF-Net | Rank-20 | 99.94 | #1 of 10 | Archive leaderboard | report |
| Person Re-Identification | iLIDS-VID | GAF-Net | Rank-5 | 99.27 | #1 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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