CASIA OpenIR
A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios
Li, Dangwei1,2; Zhang, Zhang2,3,4; Chen, Xiaotang1,2; Huang, Kaiqi2,3,5,6
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-04-01
Volume28Issue:4Pages:1575-1590
Corresponding AuthorHuang, Kaiqi(kqhuang@nlpr.ia.ac.cn)
AbstractRetrieving specific persons with various types of queries, e.g., a set of attributes or a portrait photo has great application potential in large-scale intelligent surveillance systems. In this paper, we propose a richly annotated pedestrian (RAP) dataset which serves as a unified benchmark for both attribute-based and image-based person retrieval in real surveillance scenarios. Typically, previous datasets have three improvable aspects, including limited data scale and annotation types, heterogeneous data source, and controlled scenarios. Differently, RAP is a large-scale dataset which contains 84 928 images with 72 types of attributes and additional tags of viewpoint, occlusion, body parts, and 2589 person identities. It is collected in the real uncontrolled scene and has complex visual variations in pedestrian samples due to the change of viewpoints, pedestrian postures, and cloth appearance. Towards a high-quality person retrieval benchmark, an amount of state-of-the-art algorithms on pedestrian attribute recognition and person re-identification (ReID), are performed for quantitative analysis with three evaluation tasks, i.e., attribute recognition, attribute-based and image-based person retrieval, where a new instance-based metric is proposed to measure the dependency of the prediction of multiple attributes. Finally, some interesting problems, e.g., the joint feature learning of attribute recognition and ReID, and the problem of cross-day person ReID, are explored to show the challenges and future directions in person retrieval.
KeywordPedestrian retrieval person re-identification pedestrian attribute recognition multi-label learning
DOI10.1109/TIP.2018.2878349
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB1001005] ; National Natural Science Foundation of China[61473290] ; National Natural Science Foundation of China[61673375] ; Projects of Chinese Academy of Science[QYZDB-SSW-JSC006] ; Projects of Chinese Academy of Science[173211KYSB20160008]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Projects of Chinese Academy of Science
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000451941600001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25708
Collection中国科学院自动化研究所
Corresponding AuthorHuang, Kaiqi
Affiliation1.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Syst & Engn, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
5.Ctr Res Intelligent Syst & Engn, Beijing 100190, Peoples R China
6.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Dangwei,Zhang, Zhang,Chen, Xiaotang,et al. A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(4):1575-1590.
APA Li, Dangwei,Zhang, Zhang,Chen, Xiaotang,&Huang, Kaiqi.(2019).A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(4),1575-1590.
MLA Li, Dangwei,et al."A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.4(2019):1575-1590.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Dangwei]'s Articles
[Zhang, Zhang]'s Articles
[Chen, Xiaotang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Dangwei]'s Articles
[Zhang, Zhang]'s Articles
[Chen, Xiaotang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Dangwei]'s Articles
[Zhang, Zhang]'s Articles
[Chen, Xiaotang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.