计算机工程2025，Vol.51Issue(7)：12-30,19.DOI:10.19678/j.issn.1000-3428.0069698
无监督行人重识别研究综述
Survey on Unsupervised Person Re-Identification
摘要
Abstract
The primary task of person Re-IDentification(ReID)is to identify and track a specific pedestrian across multiple non-overlapping cameras.With the development of deep neural networks and owing to the increasing demand for intelligent video surveillance,ReID has gradually attracted research attention.Most existing ReID methods primarily adopt labeled data for supervised training;however,the high annotation cost makes the scaling supervised ReID to large unlabeled datasets challenging.The paradigm of unsupervised ReID can significantly alleviate such issues.This can improve its applicability to real-life scenarios,enhancing its research potential.Although several ReID surveys have been published,they have primarily focused on supervised methods and their applications.This survey systematically reviews,analyzes,and summarizes existing ReID studies to provide a reference for researchers in this field.First,the ReID methods are comprehensively reviewed in an unsupervised setting.Based on the availability of source domain labels,the unsupervised ReID methods are categorized into unsupervised domain adaptation methods and fully unsupervised methods.Additionally,their merits and drawbacks are discussed.Subsequently,the benchmark datasets widely evaluated in ReID research are summarized,and the performance of different ReID methods on these datasets is compared.Finally,the current challenges in this field are discussed and potential future directions are proposed.关键词
行人重识别/深度神经网络/智能视频监控/无监督域适应方法/完全无监督方法Key words
person Re-Identification(ReID)/deep neural network/intelligent video surveillance/unsupervised domain adaptation method/fully unsupervised method分类
信息技术与安全科学引用本文复制引用
田青,王斌,周子枭..无监督行人重识别研究综述[J].计算机工程,2025,51(7):12-30,19.基金项目
国家自然科学基金(62176128) （62176128）
江苏省自然科学基金(BK20231143) （BK20231143）
南京大学计算机软件新技术国家重点实验室开放课题(KFKT2022B06) （KFKT2022B06）
中央高校基本科研业务费专项资金(NJ2022028) （NJ2022028）
江苏省"青蓝工程"人才项目. （）
