KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915394741075968 |
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| author | Kim, Jinseong Song, Jeonghoon Baek, Gyeongseon Noh, Byeongjoon |
| author_facet | Kim, Jinseong Song, Jeonghoon Baek, Gyeongseon Noh, Byeongjoon |
| contents | We propose \textbf{KeyRe-ID}, a keypoint-guided video-based person re-identification framework consisting of global and local branches that leverage human keypoints for enhanced spatiotemporal representation learning. The global branch captures holistic identity semantics through Transformer-based temporal aggregation, while the local branch dynamically segments body regions based on keypoints to generate fine-grained, part-aware features. Extensive experiments on MARS and iLIDS-VID benchmarks demonstrate state-of-the-art performance, achieving 91.73\% mAP and 97.32\% Rank-1 accuracy on MARS, and 96.00\% Rank-1 and 100.0\% Rank-5 accuracy on iLIDS-VID. The code for this work will be publicly available on GitHub upon publication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos Kim, Jinseong Song, Jeonghoon Baek, Gyeongseon Noh, Byeongjoon Computer Vision and Pattern Recognition Artificial Intelligence We propose \textbf{KeyRe-ID}, a keypoint-guided video-based person re-identification framework consisting of global and local branches that leverage human keypoints for enhanced spatiotemporal representation learning. The global branch captures holistic identity semantics through Transformer-based temporal aggregation, while the local branch dynamically segments body regions based on keypoints to generate fine-grained, part-aware features. Extensive experiments on MARS and iLIDS-VID benchmarks demonstrate state-of-the-art performance, achieving 91.73\% mAP and 97.32\% Rank-1 accuracy on MARS, and 96.00\% Rank-1 and 100.0\% Rank-5 accuracy on iLIDS-VID. The code for this work will be publicly available on GitHub upon publication. |
| title | KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.07393 |