KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos
Journal:
arXiv
Published Date:
Jul 10, 2025
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.
