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
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
	2019-04-01
卷号	28 期号:4 页码:1575-1590
通讯作者	Huang, Kaiqi(kqhuang@nlpr.ia.ac.cn)
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.
关键词	Pedestrian retrieval person re-identification pedestrian attribute recognition multi-label learning
DOI	10.1109/TIP.2018.2878349
收录类别	SCI
语种	英语
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] ; 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]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Projects of Chinese Academy of Science
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000451941600001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：90[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/25708
专题	复杂系统认知与决策实验室_智能系统与工程
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
第一作者单位	中国科学院自动化研究所
通讯作者单位	模式识别国家重点实验室
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.