Visible-Infrared Person Re-Identification: A Comprehensive Survey and a New Setting

计算机科学 鉴定(生物学) 模式 人工智能 不相交集 深度学习 数据科学 机器学习 数学 社会科学 植物 生物 组合数学 社会学
作者
Huantao Zheng,Xian Zhong,Wenxin Huang,Kui Jiang,Wenxuan Liu,Zheng Wang
出处
期刊:Electronics [MDPI AG]
卷期号:11 (3): 454-454 被引量:8
标识
DOI:10.3390/electronics11030454
摘要

Person re-identification (ReID) plays a crucial role in video surveillance with the aim to search a specific person across disjoint cameras, and it has progressed notably in recent years. However, visible cameras may not be able to record enough information about the pedestrian’s appearance under the condition of low illumination. On the contrary, thermal infrared images can significantly mitigate this issue. To this end, combining visible images with infrared images is a natural trend, and are considerably heterogeneous modalities. Some attempts have recently been contributed to visible-infrared person re-identification (VI-ReID). This paper provides a complete overview of current VI-ReID approaches that employ deep learning algorithms. To align with the practical application scenarios, we first propose a new testing setting and systematically evaluate state-of-the-art methods based on our new setting. Then, we compare ReID with VI-ReID in three aspects, including data composition, challenges, and performance. According to the summary of previous work, we classify the existing methods into two categories. Additionally, we elaborate on frequently used datasets and metrics for performance evaluation. We give insights on the historical development and conclude the limitations of off-the-shelf methods. We finally discuss the future directions of VI-ReID that the community should further address.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
5秒前
英姑应助dawn采纳,获得10
5秒前
清爽的火车完成签到 ,获得积分10
7秒前
科研通AI2S应助荡南桥采纳,获得10
8秒前
劲秉应助smr采纳,获得20
14秒前
14秒前
科研通AI2S应助exile采纳,获得10
14秒前
15秒前
可爱的函函应助adkis采纳,获得10
16秒前
16秒前
000完成签到 ,获得积分10
16秒前
17秒前
木鱼完成签到,获得积分10
17秒前
海棠花未眠完成签到,获得积分10
18秒前
小贺同学发布了新的文献求助10
19秒前
hu发布了新的文献求助10
19秒前
baijiayi完成签到,获得积分20
20秒前
dawn发布了新的文献求助10
22秒前
22秒前
23秒前
科研通AI2S应助exile采纳,获得10
24秒前
25秒前
26秒前
26秒前
齐天大圣完成签到,获得积分10
28秒前
adkis发布了新的文献求助10
29秒前
华仔应助小贺同学采纳,获得10
31秒前
无花果应助bjrri采纳,获得10
32秒前
科目三应助周周采纳,获得10
33秒前
赘婿应助科研通管家采纳,获得10
34秒前
34秒前
Hello应助科研通管家采纳,获得10
34秒前
爆米花应助科研通管家采纳,获得10
35秒前
充电宝应助科研通管家采纳,获得10
35秒前
打打应助科研通管家采纳,获得10
35秒前
天天快乐应助科研通管家采纳,获得10
35秒前
赘婿应助科研通管家采纳,获得10
35秒前
38秒前
天天加油完成签到 ,获得积分10
38秒前
高分求助中
Spray / Wall-interaction Modelling by Dimensionless Data Analysis 2000
ALA生合成不全マウスでの糖代謝異常の分子機構解析 520
安全防范技术与工程 500
Mathematics and Finite Element Discretizations of Incompressible Navier—Stokes Flows 500
A real-time energy management strategy based on fuzzy control and ECMS for PHEVs 400
2024 Medicinal Chemistry Reviews 400
Why I Chose China [by Morris R. Wills] in "Look", February 8 and 22, 1966; 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3191484
求助须知:如何正确求助?哪些是违规求助? 2840825
关于积分的说明 8030243
捐赠科研通 2504195
什么是DOI,文献DOI怎么找? 1337556
科研通“疑难数据库(出版商)”最低求助积分说明 638102
邀请新用户注册赠送积分活动 606622