Received January 27, 2019, accepted February 21, 2019, date of publication February 26, 2019, date of current version March 18, 2019.
Digital Object Identifier 10.1109/ACCESS.2019.2901764
Omnidirectional Feature Learning for
Person Re-Identification
DI WU
1
, HONG-WEI YANG
1
, DE-SHUANG HUANG
1
, (Senior Member, IEEE),
CHANG-AN YUAN
2
, XIAO QIN
2
, YANG ZHAO
3
, XIN-YONG ZHAO
3
,
AND JIAN-HONG SUN
3
1
School of Electronics and Information Engineering, Institute of Machine Learning and Systems Biology, Tongji University, Shanghai 201804, China
2
Science Computing and Intelligent Information Processing of Guangxi Higher Education Key Laboratory,
Guangxi Teachers Education University, Nanning 530001, China
3
Beijing E-Hualu Information Technology Co., Ltd., Beijing 100043, China
Corresponding author: De-Shuang Huang (dshuang@tongji.edu.cn)
This work was supported in part by the National Science Foundation of China under Grant 61520106006, Grant 61732012, Grant
61861146002, Grant 61772370, Grant 61702371, Grant 61672203, Grant 61572447, Grant 61772357, and Grant 61672382, in part by the
China Postdoctoral Science Foundation under Grant 2017M611619, and in part by the BAGUI Scholar Program of Guangxi Province
of China.
ABSTRACT Person re-identification (PReID) has received increasing attention due to it being an important
role in intelligent surveillance. Many state-of-the-art PReID methods are part-based deep models. Most
of these models focus on learning the part feature representation of a person’s body from the horizontal
direction. However, the feature representation of the body from the vertical direction is usually ignored.
In addition, the relationships between these part features and different feature channels are not considered.
In this paper, we introduce a multi-branch deep model for PReID. Specifically, the model consists of five
branches. Among the five branches, two branches learn the part features with spatial information from
horizontal and vertical orientations; one branch aims to learn the interdependencies between different feature
channels generated by the last convolution layer of the backbone network; the remaining two branches
are identification and triplet sub-networks in which the discriminative global feature and a corresponding
measurement can be learned simultaneously. All five branches can improve the quality of representation
learning. We conduct extensive comparison experiments on three benchmarks, including Market-1501,
CUHK03, and DukeMTMC-reID. The proposed deep framework outperforms other competitive
state-of-the-art methods. The code is available at https://github.com/caojunying/person-reidentification.
INDEX TERMS Person re-identification, deep learning, part feature, triplet model, identification model.
I. INTRODUCTION
As a fundamental task of intelligent surveillance, person
re-identification (PReID) aims to re-identify a specific person
from multiple camera views. It has been of considerable inter-
est to the computer vision community in recent years. Great
progress has been made in PReID, however, the visual appear-
ance of a person may undergo significant variations when
facing unpredictable changes in illumination, background
clutter as well as person pose, which creates a challenging
issue.
In current studies, PReID is resolved from the follow-
ing two angles: 1) Extracting discriminative descriptors to
The associate editor coordinating the review of this manuscript and
approving it for publication was Hugo Proenca.
represent different identities. 2) Learning an effective dis-
tance metric to make the relative distance between the inter-
classes larger than intra-class.
Benefiting from the considerable development of deep
learning in the computer vision community, a large number
of deep architecture-based methods have been introduced
for PReID. Different from traditional hand-crafted meth-
ods, these deep learning-based methods integrate feature
and distance metric learning in an end-to-end way. It is
worth noting that the most recent state-of-the-art results
have been achieved by deep learning-based models. Many
of them attempt to learn global pedestrian features. When
the pedestrian global features are generated by the deep
model, the Euclidean metric is applied to measure the dis-
tance between the two pedestrians. However, global feature
28402
2169-3536 2019 IEEE. Translations and content mining are permitted for academic research only.
Personal use is also permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
VOLUME 7, 2019
下载后可阅读完整内容，剩余9页未读，立即下载
姣孙孙孙
- 粉丝: 1
- 资源: 4
我的内容管理
展开
最新资源
- WebLogic集群配置与管理实战指南
- AIX5.3上安装Weblogic 9.2详细步骤
- 面向对象编程模拟试题详解与解析
- Flex+FMS2.0中文教程：开发流媒体应用的实践指南
- PID调节深入解析：从入门到精通
- 数字水印技术：保护版权的新防线
- 8位数码管显示24小时制数字电子钟程序设计
- Mhdd免费版详细使用教程：硬盘检测与坏道屏蔽
- 操作系统期末复习指南：进程、线程与系统调用详解
- Cognos8性能优化指南：软件参数与报表设计调优
- Cognos8开发入门：从Transformer到ReportStudio
- Cisco 6509交换机配置全面指南
- C#入门：XML基础教程与实例解析
- Matlab振动分析详解：从单自由度到6自由度模型
- Eclipse JDT中的ASTParser详解与核心类介绍
- Java程序员必备资源网站大全
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
