Knowledge Commons of Institute of Automation,CAS
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 | |
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. |
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