Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification | |
Zhang, La1; Guo, Haiyun2 ; Zhu, Kuan2; Qiao, Honglin3; Huang, Gaopan4; Zhang, Sen5; Zhang, Huichen5; Sun, Jian1; Wang, Jinqiao2
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| Source Publication | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
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| ISSN | 1551-6857 |
| 2022-02-01 | |
| Volume | 18Issue:1Pages:15 |
| Corresponding Author | Guo, Haiyun(haiyun.guo@nlpr.ia.ac.cn) |
| Abstract | Visible-infrared person re-identification (Re-ID) has received increasing research attention for its great practical value in night-time surveillance scenarios. Due to the large variations in person pose, viewpoint, and occlusion in the same modality, as well as the domain gap brought by heterogeneous modality, this hybrid modality person matching task is quite challenging. Different from the metric learning methods for visible person re-ID, which only pose similarity constraints on class level, an efficient metric learning approach for visible-infrared person Re-ID should take both the class-level and modality-level similarity constraints into full consideration to learn sufficiently discriminative and robust features. In this article, the hybrid modality is divided into two types, within modality and cross modality. We first fully explore the variations that hinder the ranking results of visible-infrared person re-ID and roughly summarize them into three types: within-modality variation, cross-modality modality-related variation, and cross-modality modality-unrelated variation. Then, we propose a comprehensive metric learning framework based on four kinds of paired-based similarity constraints to address all the variations within and cross modality. This framework focuses on both class-level and modality-level similarity relationships between person images. Furthermore, we demonstrate the compatibility of our framework with any paired-based loss functions by giving detailed implementation of combing it with triplet loss and contrastive loss separately. Finally, extensive experiments of our approach on SYSIJ-MM01 and RegDB demonstrate the effectiveness and superiority of our proposed metric learning framework for visible-infrared person Re-ID. |
| Keyword | Visible-infrared person re-identification cross-modality metric learning |
| DOI | 10.1145/3473341 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Key-Area Research and Development Program of Guangdong Province[2020B010165001] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[62002356] ; National Natural Science Foundation of China[61925303] ; Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety[2020ZDSYSKFKT04] |
| Funding Organization | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China ; Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods |
| WOS ID | WOS:000772639300002 |
| Publisher | ASSOC COMPUTING MACHINERY |
| Sub direction classification | 图像视频处理与分析 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/48181 |
| Collection | 模式识别国家重点实验室_图像与视频分析 |
| Corresponding Author | Guo, Haiyun |
| Affiliation | 1.Beijing Inst Technol, 5 South St, Beijing 100081, Peoples R China 2.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing, Peoples R China 3.Alibaba Cloud, Radiance JinHui Tower,Bldg 6,4th Dist, Beijing, Peoples R China 4.Alibaba Cloud, Ali Ctr, Nanjing, Jiangsu, Peoples R China 5.Minist Publ Secur, Traff Management Res Inst, 88 Qianrong Rd, Wuxi, Jiangsu, Peoples R China |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Zhang, La,Guo, Haiyun,Zhu, Kuan,et al. Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2022,18(1):15. |
| APA | Zhang, La.,Guo, Haiyun.,Zhu, Kuan.,Qiao, Honglin.,Huang, Gaopan.,...&Wang, Jinqiao.(2022).Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,18(1),15. |
| MLA | Zhang, La,et al."Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 18.1(2022):15. |
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