计算机与数字工程2025，Vol.53Issue(2)：384-388,5.DOI:10.3969/j.issn.1672-9722.2025.02.015
基于相机视角改善聚类的无监督行人重识别
Clustering Refinement Based on Camera-View for Unsupervised Person Re-Identification
摘要
Abstract
Most existing unsupervised person re-identification methods follow a clustering-based strategy,which alternates between generating pseudo labels by a clustering algorithm and training a person Re-ID model based on these pseudo labels.Howev-er,for the procedure of clustering,they ignore the feature variances of image under the change of camera-views.That is,samples within one identity may be gathered into multiple clusters according to their camera labels.Therefore,this paper proposes to rectify the instance pairs'similarity with camera-views,making them more closer to ones without the effect of the change of camera-views,which greatly facilitates the the following clustering and model training.Experiments on existing benchmark datasets demonstrate that the method is superior to most unsupervised counterparts.关键词
行人重识别/聚类/伪标签/相机视角Key words
person Re-ID/clustering/pseudo label/camera view分类
信息技术与安全科学引用本文复制引用
凌梓轩,金忠..基于相机视角改善聚类的无监督行人重识别[J].计算机与数字工程,2025,53(2):384-388,5.基金项目
国家自然科学基金项目(编号:61872188,61972204)资助. （编号:61872188,61972204）
