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

TL;DR

KeyRe-ID introduces a keypoint-guided framework for person re-identification in videos, combining global and local features for improved accuracy using human keypoints and Transformer-based temporal aggregation.

Contribution

It presents a novel part-aware, keypoint-guided approach with dual-branch architecture, achieving state-of-the-art results on major benchmarks.

Findings

Achieves 91.73% mAP on MARS

Attains 97.32% Rank-1 accuracy on MARS

Reaches 96.00% Rank-1 accuracy on iLIDS-VID

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.