CASIA OpenIR  > 类脑智能研究中心
Pedestrian Counting With Back-Propagated Information and Target Drift Remedy
Chen, Ke1; Zhang, Zhaoxiang2; Zhaoxiang Zhang
Source PublicationIEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
2017-04-01
Volume47Issue:4Pages:639-647
SubtypeArticle
AbstractPedestrian density is one of the important factors in designing visual surveillance and intelligent transportation systems, but it is challenging to obtain accurate and robust estimates because of both inconsistent crowd patterns in the scenes and target drift caused by imbalanced data distribution. Most of existing global regression frameworks focus on the former challenge to improve the robustness of regression learning, but very few work concerns on mitigating the suffering from the latter one. This paper proposes a novel counting-by-regression framework to utilize the importance of training samples to improve the robustness against inconsistent feature-target relationship based on a recently-proposed learning paradigm-learning with privileged information. To this end, the concept of back-propagation is for the first time considered to select more informative samples contributed to robust fitting performance. Moreover, the direction of target drift along the continuously-changing target dimension is discovered by learning local classifiers under different situation of pedestrian density, which can thus be exploited in our algorithm to further boost the performance. Experimental evaluation on the public UCSD and shopping Mall benchmarks verifies that our approach significantly beats the state-of-the-art counting-by-regression frameworks.
KeywordBack-propagated Cumulative Attributes Pedestrian Counting Regression Learning Visual Surveillance
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TSMC.2016.2618916
WOS KeywordAGE ESTIMATION ; CROWD DENSITY ; CLASSIFICATION ; REGRESSION ; PEOPLE
Indexed BySCI
Language英语
Funding OrganizationAcademy of Finland(267581 ; National Natural Science Foundation of China(61375036 ; 298700) ; 61511130079)
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Cybernetics
WOS IDWOS:000398966700006
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14034
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Affiliation1.Tampere Univ Technol, Dept Signal Proc, FIN-33101 Tampere, Finland
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chen, Ke,Zhang, Zhaoxiang,Zhaoxiang Zhang. Pedestrian Counting With Back-Propagated Information and Target Drift Remedy[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2017,47(4):639-647.
APA Chen, Ke,Zhang, Zhaoxiang,&Zhaoxiang Zhang.(2017).Pedestrian Counting With Back-Propagated Information and Target Drift Remedy.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,47(4),639-647.
MLA Chen, Ke,et al."Pedestrian Counting With Back-Propagated Information and Target Drift Remedy".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 47.4(2017):639-647.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chen, Ke]'s Articles
[Zhang, Zhaoxiang]'s Articles
[Zhaoxiang Zhang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Ke]'s Articles
[Zhang, Zhaoxiang]'s Articles
[Zhaoxiang Zhang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Ke]'s Articles
[Zhang, Zhaoxiang]'s Articles
[Zhaoxiang Zhang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.