行人再识别技术综述  

A Survey of Person Re-identification

作　　者：李幼蛟 卓力[1,2,4] 张菁[1,2] 李嘉锋 张辉[1,2] LI You-Jiao;ZHUO Li;ZHANG Jing;LI Jia-Feng;ZHANG Hui(Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing University of Technology,Beijing 100124;College of Microelectronics,Faculty of Information Technology,Beijing University of Technology,Beijing 100124;College of Computer Science and Technology,Shandong Uni-versity of Technology,Zibo 255000;Beijing Collaborative Innovation Center of Electric Vehicles,Beijing 100081)

机构地区：[1]北京工业大学计算智能与智能系统北京市重点实验室,北京100124 [2]北京工业大学信息学部微电子学院,北京100124 [3]山东理工大学计算机科学与技术学院,淄博255000 [4]北京电动车辆协同创新中心,北京100081

出　　处：《自动化学报》2018年第9期1554-1568,共15页Acta Automatica Sinica

基　　金：国家自然科学基金(61531006;61372149;61370189;61471013);北京市属高等学校高层次人才引进与培养计划项目(CIT&TCD20150311;CIT&TCD201404043);北京市自然科学基金(4142009;4163071);北京市教育委员会科技发展计划项目(KM201410005002;KM201510005004);北京市属高等学校人才强教计划资助项目PHR(IHLB)资助~~

摘　　要：行人再识别指的是判断不同摄像头下出现的行人是否属于同一行人,可以看作是图像检索的子问题,可以广泛应用于智能视频监控、安保、刑侦等领域.由于行人图像的分辨率变化大、拍摄角度不统一、光照条件差、环境变化大、行人姿态不断变化等原因,使得行人再识别成为目前计算机视觉领域一个既具有研究价值又极具挑战性的研究热点和难点问题.早期的行人再识别方法大多基于人工设计特征,在小规模数据集上开展研究.近年来,大规模行人再识别数据集不断推出,以及深度学习技术的迅猛发展,为行人再识别技术的发展带来了新的契机.本文对行人再识别的发展历史、研究现状以及典型方法进行梳理和总结.首先阐述了行人再识别的基本研究框架,然后分别针对行人再识别的两个关键技术(特征表达和相似性度量),进行了归纳总结,重点介绍了目前发展迅猛的深度学习技术在行人再识别中的应用.另外,本文对行人再识别中代表性的数据集以及在各个数据集上可以取得优异性能的方法进行了分析和比较.最后对行人再识别技术的未来发展趋势进行了展望.Person re-identification aims to associate the same person across different views and can be taken as a subproblem of image retrieval. It has extensive application prospects in many areas such as intelligent video surveillance,security, and criminal investigation. Due to poor illumination condition, image resolution, camera viewpoint, environment,and pedestrian pose, person re-identification has become one of the challenging problems in computer vision. Early person re-identification methods mostly rely on hand-crafted features and researches are conducted on small-scale datasets. In recent years, the emergence of large-scale datasets and rapid development of deep learning techniques provide person re-identification with new opportunities. This survey gives a detailed overview of the history, state of the art, and typical methods in this domain. Firstly, the general framework of person re-identification is presented. Then, feature representation, similarity measurement, and two key aspects of person re-identification, are further summarized, respectively.We also highlight the application of rapid developing deep learning techniques to person re-identification. Moreover, the representative datasets of person re-identification and methods of obtaining excellent performance on each dataset are analyzed and compared. Finally, the future trends of this field are discussed.

关 键 词：行人再识别 人工设计特征 深度学习 特征表达 相似性度量 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]