《计算机应用研究》|Application Research of Computers

基于深度学习的行人重识别研究综述

Research on person re-identification based on deep learning

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作者 冯霞,杜佳浩,段仪浓,刘才华
机构 中国民航大学 a.计算机科学与技术学院;b.民航信息技术科研基地,天津 300300
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文章编号 1001-3695(2020)11-004-3220-07
DOI 10.19734/j.issn.1001-3695.2019.09.0514
摘要 随着深度学习的发展,研究人员开始探索将深度学习应用于行人重识别任务并提出了大量方法,随之也迎来了新的挑战。为系统地了解这一领域的研究现状和发展趋势,首先对行人重识别任务以及存在的问题进行简单介绍;其次,根据训练方式的不同,分别探讨监督学习、半监督学习/弱监督学习以及无监督学习上行人重识别任务的研究进展,并根据现有研究热度介绍生成对抗网络和注意力机制在行人重识别上的应用;之后,列举了该领域中常用的经典数据集,并对比了深度模型在这些经典数据集(Market-1501、CUHK03等)上的表现;最后,对行人重识别领域的未来方向进行了展望。
关键词 行人重识别; 监督学习; 半监督学习; 弱监督学习; 无监督学习
基金项目 中央高校基本科研业务经费中国民航大学专项资金资助项目(3122018C024)
天津市自然科学基金资助项目(18JCYBJC85100)
中国民航大学科研启动项目(2017QD16X)
本文URL http://www.arocmag.com/article/01-2020-11-004.html
英文标题 Research on person re-identification based on deep learning
作者英文名 Feng Xia, Du Jiahao, Duan Yinong, Liu Caihua
机构英文名 a.College of Computer Science & Technology,b.Civil Aviation Information Technology Research Base,Civil Aviation University of China,Tianjin 300300,China
英文摘要 With the development of deep learning, researchers began to explore the application of deep learning to person reidentification tasks and proposed a large number of methods, which also ushered in new challenges. In order to facilitate scholars to systematically understand the research status and development trends in this field, this paper firstly introduced the person reidentification task and existing problems. Secondly, according to training methods, it discussed the research progress of the supervised learning, semi-supervised learning/weak supervision learning and unsupervised learning for the person re-identification task. It introduced the application of GAN(generative adversarial network) and attention mechanism in person re-identification following recent research hotspots. After that, it enumerated some commonly used data sets(Market-1501, CUHK03, etc. ) and their performance in this field. Finally, this paper looked forward to the future research direction of person re-identification.
英文关键词 person re-identification; supervised learning; semi-supervised learning; weakly supervised learning; unsupervised learning
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收稿日期 2019/9/3
修回日期 2019/11/1
页码 3220-3226,3240
中图分类号 TP391.41
文献标志码 A