Estimating crowd density with Minkowski fractal dimension
Autor(a) principal: | |
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Data de Publicação: | 1999 |
Outros Autores: | , , |
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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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 |
|
_version_ |
1799965279598411776 |