A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios

doi:10.1109/TIP.2018.2878349

	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
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2019-04-01
卷号	28 期号:4 页码:1575-1590
关键词	Pedestrian retrieval person re-identification pedestrian attribute recognition multi-label learning
ISSN号	1057-7149
DOI	10.1109/TIP.2018.2878349
通讯作者	Huang, Kaiqi(kqhuang@nlpr.ia.ac.cn)
英文摘要	Retrieving 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.
资助项目	National 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]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000451941600001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Projects of Chinese Academy of Science
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/25707]
专题	中国科学院自动化研究所
通讯作者	Huang, Kaiqi
作者单位	1.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
推荐引用方式 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.