Detecting pedestrians on a Movement Feature Space

Autores
Negri, Pablo Augusto; Goussies, Norberto Adrián; Lotito, Pablo Andres
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work aims at detecting pedestrians in surveillance video sequences. A pre-processing step detects motion regions on the image using a scene background model based on level lines, which generates a Movement Feature Space, and a family of oriented histogram descriptors. A cascade of boosted classifiers generates pedestrian hypotheses using this feature space. Then, a linear Support Vector Machine validates the hypotheses that are likeliest to contain a person. The combination of the three detection phases reduces false positives, preserving the majority of pedestrians. The system tests conducted in our dataset, which contain low-resolution pedestrians, achieved a maximum performance of 25.5% miss rate with a rate of 10−1 false positives per image. This value is comparable to the best detection values for this kind of images. In addition, the processing time is between 2 and 6 fps on 640 480 pixel captures. This is therefore a fast and reliable pedestrian detector.
Fil: Negri, Pablo Augusto. Universidad Arg.de la Empresa. Facultad de Ingeniería y Ciencias Exactas. Instituto de Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Goussies, Norberto Adrián. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lotito, Pablo Andres. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Pedestrian Detection
Motion Detection
Histograms of Oriented Level Lines
Adaboost Cascade
Linear Svm
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/21291
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Detecting pedestrians on a Movement Feature SpaceNegri, Pablo AugustoGoussies, Norberto AdriánLotito, Pablo AndresPedestrian DetectionMotion DetectionHistograms of Oriented Level LinesAdaboost CascadeLinear Svmhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1This work aims at detecting pedestrians in surveillance video sequences. A pre-processing step detects motion regions on the image using a scene background model based on level lines, which generates a Movement Feature Space, and a family of oriented histogram descriptors. A cascade of boosted classifiers generates pedestrian hypotheses using this feature space. Then, a linear Support Vector Machine validates the hypotheses that are likeliest to contain a person. The combination of the three detection phases reduces false positives, preserving the majority of pedestrians. The system tests conducted in our dataset, which contain low-resolution pedestrians, achieved a maximum performance of 25.5% miss rate with a rate of 10−1 false positives per image. This value is comparable to the best detection values for this kind of images. In addition, the processing time is between 2 and 6 fps on 640 480 pixel captures. This is therefore a fast and reliable pedestrian detector.Fil: Negri, Pablo Augusto. Universidad Arg.de la Empresa. Facultad de Ingeniería y Ciencias Exactas. Instituto de Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Goussies, Norberto Adrián. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lotito, Pablo Andres. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2013-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/21291Negri, Pablo Augusto; Goussies, Norberto Adrián; Lotito, Pablo Andres; Detecting pedestrians on a Movement Feature Space ; Elsevier; Pattern Recognition; 47; 1; 6-2013; 56-710031-3203CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.patcog.2013.05.020info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0031320313002446info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:41:35Zoai:ri.conicet.gov.ar:11336/21291instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:41:35.586CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Detecting pedestrians on a Movement Feature Space
title Detecting pedestrians on a Movement Feature Space
spellingShingle Detecting pedestrians on a Movement Feature Space
Negri, Pablo Augusto
Pedestrian Detection
Motion Detection
Histograms of Oriented Level Lines
Adaboost Cascade
Linear Svm
title_short Detecting pedestrians on a Movement Feature Space
title_full Detecting pedestrians on a Movement Feature Space
title_fullStr Detecting pedestrians on a Movement Feature Space
title_full_unstemmed Detecting pedestrians on a Movement Feature Space
title_sort Detecting pedestrians on a Movement Feature Space
dc.creator.none.fl_str_mv Negri, Pablo Augusto
Goussies, Norberto Adrián
Lotito, Pablo Andres
author Negri, Pablo Augusto
author_facet Negri, Pablo Augusto
Goussies, Norberto Adrián
Lotito, Pablo Andres
author_role author
author2 Goussies, Norberto Adrián
Lotito, Pablo Andres
author2_role author
author
dc.subject.none.fl_str_mv Pedestrian Detection
Motion Detection
Histograms of Oriented Level Lines
Adaboost Cascade
Linear Svm
topic Pedestrian Detection
Motion Detection
Histograms of Oriented Level Lines
Adaboost Cascade
Linear Svm
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This work aims at detecting pedestrians in surveillance video sequences. A pre-processing step detects motion regions on the image using a scene background model based on level lines, which generates a Movement Feature Space, and a family of oriented histogram descriptors. A cascade of boosted classifiers generates pedestrian hypotheses using this feature space. Then, a linear Support Vector Machine validates the hypotheses that are likeliest to contain a person. The combination of the three detection phases reduces false positives, preserving the majority of pedestrians. The system tests conducted in our dataset, which contain low-resolution pedestrians, achieved a maximum performance of 25.5% miss rate with a rate of 10−1 false positives per image. This value is comparable to the best detection values for this kind of images. In addition, the processing time is between 2 and 6 fps on 640 480 pixel captures. This is therefore a fast and reliable pedestrian detector.
Fil: Negri, Pablo Augusto. Universidad Arg.de la Empresa. Facultad de Ingeniería y Ciencias Exactas. Instituto de Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Goussies, Norberto Adrián. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lotito, Pablo Andres. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description This work aims at detecting pedestrians in surveillance video sequences. A pre-processing step detects motion regions on the image using a scene background model based on level lines, which generates a Movement Feature Space, and a family of oriented histogram descriptors. A cascade of boosted classifiers generates pedestrian hypotheses using this feature space. Then, a linear Support Vector Machine validates the hypotheses that are likeliest to contain a person. The combination of the three detection phases reduces false positives, preserving the majority of pedestrians. The system tests conducted in our dataset, which contain low-resolution pedestrians, achieved a maximum performance of 25.5% miss rate with a rate of 10−1 false positives per image. This value is comparable to the best detection values for this kind of images. In addition, the processing time is between 2 and 6 fps on 640 480 pixel captures. This is therefore a fast and reliable pedestrian detector.
publishDate 2013
dc.date.none.fl_str_mv 2013-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/21291
Negri, Pablo Augusto; Goussies, Norberto Adrián; Lotito, Pablo Andres; Detecting pedestrians on a Movement Feature Space ; Elsevier; Pattern Recognition; 47; 1; 6-2013; 56-71
0031-3203
CONICET Digital
CONICET
url http://hdl.handle.net/11336/21291
identifier_str_mv Negri, Pablo Augusto; Goussies, Norberto Adrián; Lotito, Pablo Andres; Detecting pedestrians on a Movement Feature Space ; Elsevier; Pattern Recognition; 47; 1; 6-2013; 56-71
0031-3203
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patcog.2013.05.020
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0031320313002446
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613312100171776
score 13.070432

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