| Autor: |
Boujou, Moncef, Iguernaissi, Rabah, Nicod, Lionel, Merad, Djamal, Dubuisson, Séverine |
| Předmět: |
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| Zdroj: |
Algorithms; Aug2024, Vol. 17 Issue 8, p352, 26p |
| Abstrakt: |
This study provides an in-depth analysis of GAF-Net, a novel model for video-based person re-identification (Re-ID) that matches individuals across different video sequences. GAF-Net combines appearance-based features with gait-based features derived from skeletal data, offering a new approach that diverges from traditional silhouette-based methods. We thoroughly examine each module of GAF-Net and explore various fusion methods at the both score and feature levels, extending beyond initial simple concatenation. Comprehensive evaluations on the iLIDS-VID and MARS datasets demonstrate GAF-Net's effectiveness across scenarios. GAF-Net achieves state-of-the-art 93.2 % rank-1 accuracy on iLIDS-VID's long sequences, while MARS results ( 86.09 % mAP, 89.78 % rank-1) reveal challenges with shorter, variable sequences in complex real-world settings. We demonstrate that integrating skeleton-based gait features consistently improves Re-ID performance, particularly with long, more informative sequences. This research provides crucial insights into multi-modal feature integration in Re-ID tasks, laying a foundation for the advancement of multi-modal biometric systems for diverse computer vision applications. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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