CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
A Robust Visual Person-Following Approach for Mobile Robots in Disturbing Environments
Pang, Lei1,2; Cao, Zhiqiang1,2; Yu, Junzhi1,3; Guan, Peiyu1,2; Chen, Xuechao4,5; Zhang, Weimin4,5
Source PublicationIEEE SYSTEMS JOURNAL
ISSN1932-8184
2020-06-01
Volume14Issue:2Pages:2965-2968
Abstract

This article proposes a robust visual following approach with a deep learning-based person detector, a Kalman filter (KF), and a reidentification module. The KF is introduced to predict the position of the target person, and its state is updated by the associated detection result. To deal with severe distractions and even full occlusion, the reidentification module with an identification model, a verification model, and an appearance gallery is employed in multi-person disturbing environments. Without any customized markers, the proposed approach can follow the target person steadily, and it is robust to occlusion and posture changes of the target person. Experiments results validate the effectiveness of the proposed approach.

KeywordMobile robots Detectors Cameras Visualization Robot vision systems Kalman filter (KF) mobile robot person detector person-following reidentification
DOI10.1109/JSYST.2019.2942953
WOS KeywordTRACKING
Indexed BySCI
Language英语
Funding ProjectBeijing Advanced Innovation Center for Intelligent Robots and Systems[2018IRS21] ; National Natural Science Foundation of China[61633017] ; National Natural Science Foundation of China[61633020] ; National Natural Science Foundation of China[61836015] ; Key Research and Development Program of Shandong Province[2017CXGC0925] ; Open Foundation of the State Key Laboratory of Management and Control for Complex Systems, CASIA[20190106]
Funding OrganizationBeijing Advanced Innovation Center for Intelligent Robots and Systems ; National Natural Science Foundation of China ; Key Research and Development Program of Shandong Province ; Open Foundation of the State Key Laboratory of Management and Control for Complex Systems, CASIA
WOS Research AreaComputer Science ; Engineering ; Operations Research & Management Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Operations Research & Management Science ; Telecommunications
WOS IDWOS:000543049900134
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39922
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorCao, Zhiqiang
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Peking Univ, Coll Engn, State Key Lab Turbulence & Complex Syst, BIC ESAr,Dept Mech & Engn Sci, Beijing 100871, Peoples R China
4.Beijing Adv Innovat Ctr Intelligent Robots & Syst, Beijing 100081, Peoples R China
5.Beijing Inst Technol, Intelligent Robot Inst, Beijing 100081, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Pang, Lei,Cao, Zhiqiang,Yu, Junzhi,et al. A Robust Visual Person-Following Approach for Mobile Robots in Disturbing Environments[J]. IEEE SYSTEMS JOURNAL,2020,14(2):2965-2968.
APA Pang, Lei,Cao, Zhiqiang,Yu, Junzhi,Guan, Peiyu,Chen, Xuechao,&Zhang, Weimin.(2020).A Robust Visual Person-Following Approach for Mobile Robots in Disturbing Environments.IEEE SYSTEMS JOURNAL,14(2),2965-2968.
MLA Pang, Lei,et al."A Robust Visual Person-Following Approach for Mobile Robots in Disturbing Environments".IEEE SYSTEMS JOURNAL 14.2(2020):2965-2968.
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