中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 复杂系统认知与决策实验室
  5. 智能系统与工程
A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios

文献类型：期刊论文

作者Li, Dangwei1,2; Zhang, Zhang2,3,4; Chen, Xiaotang1,2; Huang, Kaiqi2,3,5,6
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期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
DOI10.1109/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
WOS记录号WOS:000451941600001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Projects of Chinese Academy of Science
源URL[http://ir.ia.ac.cn/handle/173211/25707]  
专题智能系统与工程
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
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。