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dc.contributor.authorMarana, A. N.
dc.contributor.authorVelastin, S. A.
dc.contributor.authorCosta, L. F.
dc.contributor.authorLotufo, R. A.
dc.date.accessioned2014-02-26T17:15:41Z
dc.date.accessioned2014-05-20T14:16:03Z
dc.date.available2014-02-26T17:15:41Z
dc.date.available2014-05-20T14:16:03Z
dc.date.issued1998-04-01
dc.identifierhttp://dx.doi.org/10.1016/S0925-7535(97)00081-7
dc.identifier.citationSafety Science. Amsterdam: Elsevier B.V., v. 28, n. 3, p. 165-175, 1998.
dc.identifier.issn0925-7535
dc.identifier.urihttp://hdl.handle.net/11449/24818
dc.description.abstractThis paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented. (C) 1998 Elsevier B.V. Ltd. All rights reserved.en
dc.format.extent165-175
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofSafety Science
dc.sourceWeb of Science
dc.subjectcrowd densitypt
dc.subjecttexturept
dc.subjectneural networkpt
dc.titleAutomatic estimation of crowd density using textureen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationUNESP, IGCE, DEMAC, Rio Claro, SP, Brazil
dc.description.affiliationUnespUNESP, IGCE, DEMAC, Rio Claro, SP, Brazil
dc.identifier.doi10.1016/S0925-7535(97)00081-7
dc.identifier.wosWOS:000075454300003
dc.rights.accessRightsAcesso restrito
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Geociências e Ciências Exatas, Rio Claropt
unesp.author.orcid0000-0003-4861-7061[1]
dc.relation.ispartofjcr2.835
dc.relation.ispartofsjr1,113
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