Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/100622
Type: Artigo de periódico
Title: Automatic Estimation Of Crowd Density Using Texture
Author: Marana A.N.
Velastin S.A.
Costa L.F.
Lotufo R.A.
Abstract: This 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.This 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 organizing 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.
Editor: Elsevier Sci Ltd, Exeter, United Kingdom
Rights: fechado
Identifier DOI: 10.1016/S0925-7535(97)00081-7
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0032052162&partnerID=40&md5=e0b2c449cefe53a3c70bba0a467aff5b
Date Issue: 1998
Appears in Collections:Unicamp - Artigos e Outros Documentos

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