现代电子技术2026，Vol.49Issue(6)：174-183,10.DOI:10.16652/j.issn.1004-373x.2026.06.026
基于局部特征匹配和伪标签细化的纯无监督行人重识别
Purely unsupervised person ReID based on local feature matching and pseudo-label refinement
摘要
Abstract
In allusion to the problem of significant noise in pseudo-labels generated by clustering in unsupervised person re-identification(ReID),a purely unsupervised method based on local feature matching and pseudo-label refinement is proposed.This method does not rely on any source domain information,but only considers the correlation between samples at the image level and assigns robust pseudo-labels for training.A local feature matching module is designed to align and rank local features of samples,so as to represent the correlation between global features and local features of samples reasonably.Then,a correlation scoring module is used to score the rationality of the generated pseudo-labels by considering the correlation between global features and local features comprehensively.On this basis,a pseudo-label refinement module is introduced to refine the pseudo-labels of global features and local features based on the scores of samples.The refined pseudo-labels are used to train the net-work and continuously update the pseudo-labels.The experimental verification of the method is conducted on the public per-son ReID datasets Market-1501,DukeMTMC-ReID and MSMT17.The results show that the mAP of this method can reach 81.9%,71.1%and 31.6%on the Market-1501,DukeMTMC-ReID and MSMT17 datasets,respectively,demonstrating better per-formance.关键词
行人重识别/无监督/伪标签细化/局部特征匹配/神经网络/消融实验/相关性评分Key words
person re-identification/unsupervised/pseudo-label refinement/local feature matching/neural network/ablation experiment/correlation score分类
信息技术与安全科学引用本文复制引用
刘国权,陈尚良,秦晨旭,周书民,周焕银,王小刚..基于局部特征匹配和伪标签细化的纯无监督行人重识别[J].现代电子技术,2026,49(6):174-183,10.基金项目
国家自然科学基金资助项目(62341301) （62341301）
国家自然科学基金资助项目(62063001) （62063001）
国家自然科学基金资助项目(12165001) （12165001）
人工智能四川省重点实验室开放基金(2023RYY02) （2023RYY02）
特殊环境机器人技术四川省重点实验室开放基金(23kftk06) （23kftk06）
