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Article: People counting and human detection in a challenging situation

TitlePeople counting and human detection in a challenging situation
Authors
KeywordsExpectation-maximum
human detection
neural network
people counting
Issue Date2011
PublisherIEEE.
Citation
Ieee Transactions On Systems, Man, And Cybernetics Part A:Systems And Humans, 2011, v. 41 n. 1, p. 24-33 How to Cite?
AbstractReliable people counting and human detection is an important problem in visual surveillance. In recent years, the field has seen many advances, but the solutions have restrictions: people must be moving, the background must be simple, and the image resolution must be high. This paper aims to develop an effective method for estimating the number of people and locate each individual in a low resolution image with complicated scenes. The contribution of this paper is threefold. First, postprocessing steps are performed on background subtraction results to estimate the number of people in a complicated scene, which includes people who are moving only slightly. Second, an Expectation Maximization (EM)-based method has been developed to locate individuals in a low resolution scene. In this method, a new cluster model is used to represent each person in the scene. The method does not require a very accurate foreground contour. Third, the number of people is used as a priori for locating individuals based on feature points. Hence, the methods for estimating the number of people and for locating individuals are connected. The developed methods have been validated based on a 4-hour video, with the number of people in the scene ranging from 36 to 222. The best result for estimating the number of people has an average error of 10% over 51 test cases. Based on the estimated number of people, some results of the EM-based method have also been shown. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/73494
ISSN
2012 Impact Factor: 2.183
2015 SCImago Journal Rankings: 1.458
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHou, YLen_HK
dc.contributor.authorPang, GKHen_HK
dc.date.accessioned2010-09-06T06:51:51Z-
dc.date.available2010-09-06T06:51:51Z-
dc.date.issued2011en_HK
dc.identifier.citationIeee Transactions On Systems, Man, And Cybernetics Part A:Systems And Humans, 2011, v. 41 n. 1, p. 24-33en_HK
dc.identifier.issn1083-4427en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73494-
dc.description.abstractReliable people counting and human detection is an important problem in visual surveillance. In recent years, the field has seen many advances, but the solutions have restrictions: people must be moving, the background must be simple, and the image resolution must be high. This paper aims to develop an effective method for estimating the number of people and locate each individual in a low resolution image with complicated scenes. The contribution of this paper is threefold. First, postprocessing steps are performed on background subtraction results to estimate the number of people in a complicated scene, which includes people who are moving only slightly. Second, an Expectation Maximization (EM)-based method has been developed to locate individuals in a low resolution scene. In this method, a new cluster model is used to represent each person in the scene. The method does not require a very accurate foreground contour. Third, the number of people is used as a priori for locating individuals based on feature points. Hence, the methods for estimating the number of people and for locating individuals are connected. The developed methods have been validated based on a 4-hour video, with the number of people in the scene ranging from 36 to 222. The best result for estimating the number of people has an average error of 10% over 51 test cases. Based on the estimated number of people, some results of the EM-based method have also been shown. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humansen_HK
dc.rightsIEEE Transactions on Systems, Man and Cybernetics, Part A: Systems & Humans. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectExpectation-maximumen_HK
dc.subjecthuman detectionen_HK
dc.subjectneural networken_HK
dc.subjectpeople countingen_HK
dc.titlePeople counting and human detection in a challenging situationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1083-4427&volume=41&issue=1&spage=24&epage=33&date=2011&atitle=People+counting+and+human+detection+in+a+challenging+situation-
dc.identifier.emailPang, GKH:gpang@eee.hku.hken_HK
dc.identifier.authorityPang, GKH=rp00162en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TSMCA.2010.2064299en_HK
dc.identifier.scopuseid_2-s2.0-78349311901en_HK
dc.identifier.hkuros168560en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78349311901&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume41en_HK
dc.identifier.issue1en_HK
dc.identifier.spage24en_HK
dc.identifier.epage33en_HK
dc.identifier.isiWOS:000284095400003-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridHou, YL=25651509000en_HK
dc.identifier.scopusauthoridPang, GKH=7103393283en_HK
dc.identifier.citeulike8638235-

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