计算机工程与应用2024，Vol.60Issue(1)：227-235,9.DOI:10.3778/j.issn.1002-8331.2207-0469
二次聚类的无监督行人重识别方法
Unsupervised Person Re-Identification Based on Quadratic Clustering
摘要
Abstract
In view of the influence of objective factors such as hardware differences and illumination changes,the current unsupervised person re-identification method leads to a large contrast in the image of the same person,which is easy to cause the problem of wrong pseudo-labels generation of samples,which makes the existing unsupervised person re-identification method.There is still room for further improvement in the identification method.To solve this problem,this paper proposes an unsupervised person re-identification based on quadratic clustering method.This method mainly includes global quadratic clustering module and supervised learning module based on quadratic clustering results.Specifi-cally,the former performs unsupervised analysis of camera ID and pedestrian ID based on global quadratic clustering,which solves the problem of unified imaging style of the same pedestrian under different camera perspectives;the latter uses supervised learning to improve memory.The initialization and update method of the dictionary solves the problem of model offset during training.Through the co-training of this dual module,it can jointly suppress the problem of false labels generated by images collected across cameras.The algorithm proposed in this paper is tested on Market-1501,DukeMTMC-ReID,MSMT17,Person and VeRi-776 datasets,respectively,and achieves mAP=81.2%and rank-1=91.2%,mAP=68.4%and rank-1=78.7%,mAP=31.1%and rank-1=60.4%,mAP=88.3%and rank-1=93.6%,compared with the current state-of-the-art methods,they have improved by 2.4,1.8,6.0,2.5 and 4.3 percentage points rank-1 accuracy.关键词
行人重识别/无监督学习/二次聚类/协同训练Key words
person re-identification/unsupervised learning/quadratic clustering/collaborative training分类
信息技术与安全科学引用本文复制引用
熊明福,肖应雄,陈佳,胡新荣,彭涛..二次聚类的无监督行人重识别方法[J].计算机工程与应用,2024,60(1):227-235,9.基金项目
湖北省自然科学基金面上项目(2021CFB568) （2021CFB568）
国家重点研发计划项目(2021YFF0602102). （2021YFF0602102）
