KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos
AI-ready brief
We propose KeyRe-ID, a keypoint-guided video-based person re-identification frame- work that integrates global and local modeling. The global branch captures holistic identity semantics via Transformer-based temporal aggregation, while the local branch utilizes the proposed Keypoint-guided Part Segmentation (KPS) module.
Author abstract
We propose KeyRe-ID, a keypoint-guided video-based person re-identification frame- work that integrates global and local modeling. The global branch captures holistic identity semantics via Transformer-based temporal aggregation, while the local branch utilizes the proposed Keypoint-guided Part Segmentation (KPS) module. KPS dy- namically assigns soft attention weights, which naturally suppresses features from occluded body parts to filter out background noise. This generates anatomically aligned part features aggregated at the clip level for temporal consistency. To fur- ther enhance robustness against pose variation and misalignment, we incorporate the Temporal Clip Shift and Shu ffle (TCSS) mechanism to induce temporal invariance. By jointly leveraging global cues and dynamic part-aware representations, KeyRe-ID achieves strong discriminability. Extensive experiments on MARS and iLIDS-VID demonstrate state-of-the-art performance, achieving 91.73% mAP and 97.32% Rank- 1 accuracy on MARS and 96.00% Rank-1 on iLIDS-VID. Implementation details are available at: https://github.com/JinSeong0115/KeyRe-ID ⋆This work was supported by the Soonchunhyang University Research Fund. ∗Corresponding author Email address: powernoh@sch.ac.kr (Byeongjoon Noh) 1These authors contributed equally to this work. Preprint submitted to Elsevier January 22, 2026
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Citation-ready BibTeX
@unpublished{noh2026keyreidkeypointguidedper,
title = {KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos},
author = {J. Kim and J. Song and G. Baek and B. Noh},
year = {2026},
journal = {Pattern Recognition ·},
note = {Under review}
}