Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established v...

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Detalles Bibliográficos
Autores: Rosas-Arias, Leonel, Portillo-Portillo, Jose, Hernández-Suárez, Aldo, Olivares-Mercado, Jesus, Sánchez-Pérez, Gabriel, Toscano-Medina, Karina, Pérez-Meana, Hector, Sandoval Orozco, Ana Lucila, García Villalba, Luis Javier
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/12687
Acceso en línea:https://hdl.handle.net/20.500.14352/12687
Access Level:acceso abierto
Palabra clave:video processing
motion detection
incremental learning
Incremental PCA
traffic flow
Informática (Informática)
Inteligencia artificial (Informática)
1203.17 Informática
1203.04 Inteligencia Artificial
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oai_identifier_str oai:docta.ucm.es:20.500.14352/12687
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network_name_str España
repository_id_str
spelling Vehicle Counting in Video Sequences: An Incremental Subspace Learning ApproachRosas-Arias, LeonelPortillo-Portillo, JoseHernández-Suárez, AldoOlivares-Mercado, JesusSánchez-Pérez, GabrielToscano-Medina, KarinaPérez-Meana, HectorSandoval Orozco, Ana LucilaGarcía Villalba, Luis Javiervideo processingmotion detectionincremental learningIncremental PCAtraffic flowInformática (Informática)Inteligencia artificial (Informática)1203.17 Informática1203.04 Inteligencia ArtificialThe counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.MDPIUniversidad Complutense de Madrid20192019-01-0120192019-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/12687reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/126872026-06-02T12:44:21Z
dc.title.none.fl_str_mv Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach
title Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach
spellingShingle Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach
Rosas-Arias, Leonel
video processing
motion detection
incremental learning
Incremental PCA
traffic flow
Informática (Informática)
Inteligencia artificial (Informática)
1203.17 Informática
1203.04 Inteligencia Artificial
title_short Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach
title_full Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach
title_fullStr Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach
title_full_unstemmed Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach
title_sort Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach
dc.creator.none.fl_str_mv Rosas-Arias, Leonel
Portillo-Portillo, Jose
Hernández-Suárez, Aldo
Olivares-Mercado, Jesus
Sánchez-Pérez, Gabriel
Toscano-Medina, Karina
Pérez-Meana, Hector
Sandoval Orozco, Ana Lucila
García Villalba, Luis Javier
author Rosas-Arias, Leonel
author_facet Rosas-Arias, Leonel
Portillo-Portillo, Jose
Hernández-Suárez, Aldo
Olivares-Mercado, Jesus
Sánchez-Pérez, Gabriel
Toscano-Medina, Karina
Pérez-Meana, Hector
Sandoval Orozco, Ana Lucila
García Villalba, Luis Javier
author_role author
author2 Portillo-Portillo, Jose
Hernández-Suárez, Aldo
Olivares-Mercado, Jesus
Sánchez-Pérez, Gabriel
Toscano-Medina, Karina
Pérez-Meana, Hector
Sandoval Orozco, Ana Lucila
García Villalba, Luis Javier
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv video processing
motion detection
incremental learning
Incremental PCA
traffic flow
Informática (Informática)
Inteligencia artificial (Informática)
1203.17 Informática
1203.04 Inteligencia Artificial
topic video processing
motion detection
incremental learning
Incremental PCA
traffic flow
Informática (Informática)
Inteligencia artificial (Informática)
1203.17 Informática
1203.04 Inteligencia Artificial
description The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01
2019
2019-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/12687
url https://hdl.handle.net/20.500.14352/12687
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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