KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos
This work addresses the problem of accurately identifying individuals across video frames for surveillance and security applications, representing an incremental improvement over existing methods.
The paper tackled video-based person re-identification by proposing KeyRe-ID, a framework that uses human keypoints to guide global and local feature learning, achieving state-of-the-art results such as 91.73% mAP and 97.32% Rank-1 accuracy on the MARS benchmark.
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.