基于深度学习的行人重识别综述

doi:10.11772/j.issn.1001-9081.2019091703

计算机应用 ›› 2020, Vol. 40 ›› Issue (5): 1243-1252.DOI: 10.11772/j.issn.1001-9081.2019091703

• 人工智能 • 下一篇

基于深度学习的行人重识别综述

杨锋^1,2, 许玉¹, 尹梦晓^1,2, 符嘉成¹, 黄冰¹, 梁芳烜¹

1.广西大学计算机与电子信息学院，南宁 530004
2.广西多媒体通信与网络技术重点实验室(广西大学)，南宁 530004

收稿日期:2019-10-10 修回日期:2019-12-16 出版日期:2020-05-10 发布日期:2020-05-15
通讯作者: 杨锋(1979—)
作者简介:杨锋(1979—)，男，广西玉林人，副教授，博士，CCF会员，主要研究方向：人工智能、网络信息安全、大数据与高性能计算、精准医学；许玉(1993—)，女，广西百色人，硕士研究生，主要研究方向：深度学习、行人重识别；尹梦晓(1978—)，女，河南南阳人，副教授，博士， CCF会员，主要研究方向：计算机图形学与虚拟现实、数字几何处理、图像与视频编辑、图论；符嘉成（1995—），男，广西柳州人，硕士研究生，主要研究方向：深度学习、医学图像配准；黄冰（1993—），女，广西南宁人，硕士研究生，主要研究方向：医学图像融合；梁芳烜（1994—），女，广西横县人，硕士研究生，主要研究方向：深度学习、图像分割。
基金资助:
国家自然科学基金资助项目(61861004, 61762007)；广西自然科学基金资助项目(2017GXNSFAA198267,2017GXNSFAA198269)。

Review on deep learning-based pedestrian re-identification

YANG Feng^1,2, XU Yu¹, YIN Mengxiao^1,2, FU Jiacheng¹, HUANG Bing¹, LIANG Fangxuan¹

1.School of Computer, Electronics and Information, Guangxi University, NanningGuangxi 530004, China
2.Guangxi Key Laboratory of Multimedia Communications Network Technology (Guangxi University), NanningGuangxi 530004, China

Received:2019-10-10 Revised:2019-12-16 Online:2020-05-10 Published:2020-05-15
Contact: YANG Feng, born in 1979, Ph. D., associate professor. His research interests include artificial intelligence, network information security, big data and high-performance computing, precision medicine.
About author:YANG Feng, born in 1979, Ph. D., associate professor. His research interests include artificial intelligence, network information security, big data and high-performance computing, precision medicine.XU Yu, born in 1993, M. S. candidate. Her research interests include deep learning, pedestrian re-identification.YIN Mengxiao, born in 1978，Ph. D., associate professor. Her research interests include computer graphics and virtual reality, digital geometry processing, image and video editing, graph theory.FU Jiacheng, born in 1995, M. S. candidate. His research interests include deep learning, medical image registration.HUANG Bing, born in 1993, M. S. candidate. Her research interests include medical image fusion.LIANG Fangxuan, born in 1994, M. S. candidate. Her research interests include deep learning, image segmentation.
Supported by:
This work was partially supported by the National Natural Science Foundation of China (61861004, 61762007, the Natural Science Foundation of Guangxi (2017GXNSFAA198267, 2017GXNSFAA198269).

摘要/Abstract

摘要： 行人重识别(Re-ID)是计算机视觉领域的热点问题，主要研究的是“如何关联位于不同物理位置的不同摄像机捕获到的特定人员的问题”。传统的行人Re-ID方法主要基于底层特征如局部描述符、颜色直方图和人体姿势的提取。近几年，针对行人遮挡和姿势不对齐等传统方法所遗留问题，业内提出了基于区域、注意力机制、姿势和生成对抗性网络(GAN)等深度学习的行人Re-ID方法，实验结果得到较明显的提高。故对深度学习在行人Re-ID中的研究进行了总结和分类，区别于以前的综述，将行人重识别方法分成四大类来讨论。首先,通过区域、注意力、姿势和GAN四类方法来综述基于深度学习的行人Re-ID方法;然后,分析这些方法在主流数据集上的mAP和Rank-1指标性能表现，结果显示基于深度学习的方法可以增强局部特征之间的联系并缩小域间隙，从而减少模型过拟合;最后,展望了行人Re-ID方法研究的发展方向。

关键词: 行人重识别, 深度学习, 生成对抗性网络, 区域, 注意力, 姿势

Abstract: Pedestrian Re-IDentification (Re-ID) is a hot issue in the field of computer vision and mainly focuses on “how to relate to specific person captured by different cameras in different physical locations”. Traditional methods of Re-ID were mainly based on the extraction of low-level features, such as local descriptors, color histograms and human poses. In recent years, in view of the problems in traditional methods such as pedestrian occlusion and posture disalignment, pedestrian Re-ID methods based on deep learning such as region, attention mechanism, posture and Generative Adversarial Network (GAN) were proposed and the experimental results became significantly better than before. Therefore, the researches of deep learning in pedestrian Re-ID were summarized and classified, and different from the previous reviews, the pedestrian Re-ID methods were divided into four categories to discuss in this review. Firstly, the pedestrian Re-ID methods based on deep learning were summarized by following four methods region, attention, posture, and GAN. Then the performances of mAP (mean Average Precision) and Rank-1 indicators of these methods on the mainstream datasets were analyzed. The results show that the deep learning-based methods can reduce the model overfitting by enhancing the connection between local features and narrowing domain gaps. Finally, the development direction of pedestrian Re-ID method research was forecasted.

Key words: Re-IDentification (Re-ID), deep learning, Generative Adversarial Network (GAN), region, attention, pose

中图分类号:

TP391.4

杨锋, 许玉, 尹梦晓, 符嘉成, 黄冰, 梁芳烜. 基于深度学习的行人重识别综述[J]. 计算机应用, 2020, 40(5): 1243-1252.

YANG Feng, XU Yu, YIN Mengxiao, FU Jiacheng, HUANG Bing, LIANG Fangxuan. Review on deep learning-based pedestrian re-identification[J]. Journal of Computer Applications, 2020, 40(5): 1243-1252.

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基于深度学习的行人重识别综述

Review on deep learning-based pedestrian re-identification

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