Estimating crowd density with Minkowski fractal dimension

Detalhes bibliográficos
Autor(a) principal: Marana, Aparecido Nilceu [UNESP]
Data de Publicação: 1999
Outros Autores: Costa, L. da F [UNESP], Lotufo, R. A. [UNESP], Velastin, S. A. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ICASSP.1999.757602
http://hdl.handle.net/11449/65690
Resumo: The estimation of the number of people in an area under surveillance is very important for the problem of crowd monitoring. When an area reaches an occupation level greater than the projected one, people's safety can be in danger. This paper describes a new technique for crowd density estimation based on Minkowski fractal dimension. Fractal dimension has been widely used to characterize data texture in a large number of physical and biological sciences. The results of our experiments show that fractal dimension can also be used to characterize levels of people congestion in images of crowds. The proposed technique is compared with a statistical and a spectral technique, in a test study of nearly 300 images of a specific area of the Liverpool Street Railway Station, London, UK. Results obtained in this test study are presented.
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spelling Estimating crowd density with Minkowski fractal dimensionEstimationPopulation statisticsSpectrum analysisCrowd density estimationMinkowski fractal dimensionFractalsThe estimation of the number of people in an area under surveillance is very important for the problem of crowd monitoring. When an area reaches an occupation level greater than the projected one, people's safety can be in danger. This paper describes a new technique for crowd density estimation based on Minkowski fractal dimension. Fractal dimension has been widely used to characterize data texture in a large number of physical and biological sciences. The results of our experiments show that fractal dimension can also be used to characterize levels of people congestion in images of crowds. The proposed technique is compared with a statistical and a spectral technique, in a test study of nearly 300 images of a specific area of the Liverpool Street Railway Station, London, UK. Results obtained in this test study are presented.UNESP, Rio ClaroUNESP, Rio ClaroUniversidade Estadual Paulista (Unesp)Marana, Aparecido Nilceu [UNESP]Costa, L. da F [UNESP]Lotufo, R. A. [UNESP]Velastin, S. A. [UNESP]2014-05-27T11:19:41Z2014-05-27T11:19:41Z1999-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject3521-3524http://dx.doi.org/10.1109/ICASSP.1999.757602ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 6, p. 3521-3524.0736-77911520-6149http://hdl.handle.net/11449/6569010.1109/ICASSP.1999.757602WOS:0000796907008832-s2.0-00326813116027713750942689Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings0,402info:eu-repo/semantics/openAccess2024-04-23T16:11:27Zoai:repositorio.unesp.br:11449/65690Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:27Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Estimating crowd density with Minkowski fractal dimension
title Estimating crowd density with Minkowski fractal dimension
spellingShingle Estimating crowd density with Minkowski fractal dimension
Marana, Aparecido Nilceu [UNESP]
Estimation
Population statistics
Spectrum analysis
Crowd density estimation
Minkowski fractal dimension
Fractals
title_short Estimating crowd density with Minkowski fractal dimension
title_full Estimating crowd density with Minkowski fractal dimension
title_fullStr Estimating crowd density with Minkowski fractal dimension
title_full_unstemmed Estimating crowd density with Minkowski fractal dimension
title_sort Estimating crowd density with Minkowski fractal dimension
author Marana, Aparecido Nilceu [UNESP]
author_facet Marana, Aparecido Nilceu [UNESP]
Costa, L. da F [UNESP]
Lotufo, R. A. [UNESP]
Velastin, S. A. [UNESP]
author_role author
author2 Costa, L. da F [UNESP]
Lotufo, R. A. [UNESP]
Velastin, S. A. [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Marana, Aparecido Nilceu [UNESP]
Costa, L. da F [UNESP]
Lotufo, R. A. [UNESP]
Velastin, S. A. [UNESP]
dc.subject.por.fl_str_mv Estimation
Population statistics
Spectrum analysis
Crowd density estimation
Minkowski fractal dimension
Fractals
topic Estimation
Population statistics
Spectrum analysis
Crowd density estimation
Minkowski fractal dimension
Fractals
description The estimation of the number of people in an area under surveillance is very important for the problem of crowd monitoring. When an area reaches an occupation level greater than the projected one, people's safety can be in danger. This paper describes a new technique for crowd density estimation based on Minkowski fractal dimension. Fractal dimension has been widely used to characterize data texture in a large number of physical and biological sciences. The results of our experiments show that fractal dimension can also be used to characterize levels of people congestion in images of crowds. The proposed technique is compared with a statistical and a spectral technique, in a test study of nearly 300 images of a specific area of the Liverpool Street Railway Station, London, UK. Results obtained in this test study are presented.
publishDate 1999
dc.date.none.fl_str_mv 1999-01-01
2014-05-27T11:19:41Z
2014-05-27T11:19:41Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/ICASSP.1999.757602
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 6, p. 3521-3524.
0736-7791
1520-6149
http://hdl.handle.net/11449/65690
10.1109/ICASSP.1999.757602
WOS:000079690700883
2-s2.0-0032681311
6027713750942689
url http://dx.doi.org/10.1109/ICASSP.1999.757602
http://hdl.handle.net/11449/65690
identifier_str_mv ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 6, p. 3521-3524.
0736-7791
1520-6149
10.1109/ICASSP.1999.757602
WOS:000079690700883
2-s2.0-0032681311
6027713750942689
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
0,402
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 3521-3524
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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