KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos

• URL: http://arxiv.org/abs/2507.07393v3
• Date: Thu, 17 Jul 2025 02:04:22 GMT
• Title: KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos
• Authors: Jinseong Kim, Jeonghoon Song, Gyeongseon Baek, Byeongjoon Noh,
• Abstract summary: We propose a keypoint-guided video-based person reidentification framework consisting of global and local branches that leverage human keypoints for enhanced learning.<n>Experiments on MARS benchmarks demonstrate state-of-the-art performance, achieving 91.73% mAP and 97.32% Rank-1 accuracy.<n>The code for this work will be publicly available upon publication on GitHub.
• Score: 0.07499722271664144
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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