Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors

Background subtraction is one of the key pre-processing steps necessary for obtaining relevant information from a video sequence. The selection of a background subtraction algorithm and its parameters is also important for achieving optimal detection performance, especially in night environments. Th...

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Detalhes bibliográficos
Autores: Alonso, Mercedes, Brunete, Alberto, Hernando, Miguel, Gambao, Ernesto
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/215512
Acesso em linha:http://hdl.handle.net/10261/215512
Access Level:acceso abierto
Palavra-chave:Cameras
Detectors
Genetic algorithms
Senior citizens
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spelling Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall DetectorsAlonso, MercedesBrunete, AlbertoHernando, MiguelGambao, ErnestoCamerasDetectorsGenetic algorithmsSenior citizensBackground subtraction is one of the key pre-processing steps necessary for obtaining relevant information from a video sequence. The selection of a background subtraction algorithm and its parameters is also important for achieving optimal detection performance, especially in night environments. The research contribution presented in this paper is the identification of the optimal background subtractor algorithm in indoor night-time environments, with a focus on the detection of human falls. 30 background subtraction algorithms are analyzed to determine which has the best performance in indoor night-time environments. Genetic algorithms have been applied to identify the best background subtraction algorithm, to optimize the background subtractor parameters and to calculate the optimal number of pre- and post-processing operations. The results show that the best algorithm for fall-detection in indoor, night-time environments is the LBAdaptativeSOM, optimal parameters and processing operations for this algorithm are reported.The research leading to these results has received funding from RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by “Programas de Actividades I+D en la Comunidad de Madrid” and co-financed by Structural Funds of the EU.Peer reviewedInstitute of Electrical and Electronics EngineersComunidad de MadridEuropean CommissionBrunete, Alberto [0000-0001-9873-232X]Hernando, Miguel [0000-0001-9997-0266]Gambao, Ernesto [0000-0003-1705-1800]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/215512reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#S2018/NMT-4331/RoboCity2030-DIH-CMSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2155122026-05-22T06:33:51Z
dc.title.none.fl_str_mv Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
title Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
spellingShingle Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
Alonso, Mercedes
Cameras
Detectors
Genetic algorithms
Senior citizens
title_short Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
title_full Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
title_fullStr Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
title_full_unstemmed Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
title_sort Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
dc.creator.none.fl_str_mv Alonso, Mercedes
Brunete, Alberto
Hernando, Miguel
Gambao, Ernesto
author Alonso, Mercedes
author_facet Alonso, Mercedes
Brunete, Alberto
Hernando, Miguel
Gambao, Ernesto
author_role author
author2 Brunete, Alberto
Hernando, Miguel
Gambao, Ernesto
author2_role author
author
author
dc.contributor.none.fl_str_mv Comunidad de Madrid
European Commission
Brunete, Alberto [0000-0001-9873-232X]
Hernando, Miguel [0000-0001-9997-0266]
Gambao, Ernesto [0000-0003-1705-1800]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Cameras
Detectors
Genetic algorithms
Senior citizens
topic Cameras
Detectors
Genetic algorithms
Senior citizens
description Background subtraction is one of the key pre-processing steps necessary for obtaining relevant information from a video sequence. The selection of a background subtraction algorithm and its parameters is also important for achieving optimal detection performance, especially in night environments. The research contribution presented in this paper is the identification of the optimal background subtractor algorithm in indoor night-time environments, with a focus on the detection of human falls. 30 background subtraction algorithms are analyzed to determine which has the best performance in indoor night-time environments. Genetic algorithms have been applied to identify the best background subtraction algorithm, to optimize the background subtractor parameters and to calculate the optimal number of pre- and post-processing operations. The results show that the best algorithm for fall-detection in indoor, night-time environments is the LBAdaptativeSOM, optimal parameters and processing operations for this algorithm are reported.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/215512
url http://hdl.handle.net/10261/215512
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
S2018/NMT-4331/RoboCity2030-DIH-CM
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869412379014463488
score 15,812429

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