Smoke detection using simplified descriptors of video information

Autores
Monte, Gustavo; Marasco, Damian; Pastore, Juan Ignacio; Liscovsky, Pablo; Ballarin, Virginia
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Automatic visual detection of smoke in confined or open spaces is overriding to issue early warnings that can save lives or prevent irreparable damage. While fire presents a range of characteristic colour, smoke does not present a readily apparent pattern. Changes its shape, does not contain clear edges, presents a chaotic behaviour and colour manifests from white to black, including all nuances. This paper presents an algorithm that efficiently pre-process a frame that extracts the main component of information, decreasing orders of magnitude the source size. From this new structure, algorithms based on the temporal and spatial change of subsets of the new structure are applied. Decision is based on fusion of weak classifiers. The algorithms are described and validated with experimental results of real-time detection for open and confined spaces, considering simplicity and efficiency of the proposed method suitable for embedded systems.
Fil: Monte, Gustavo. Universidad Tecnológica Nacional. Facultad Regional Del Neuquen ; Argentina.
Fil: Marasc, Damian. Universidad Tecnológica Nacional. Facultad Regional Del Neuquen ; Argentina.
Fil: Liscovsky, Pablo. Universidad Tecnológica Nacional. Facultad Regional Del Neuquen ; Argentina.
Fil: Ballarin, Virginia. Universidad Nacional de Mar del Plata. Facultad de Ingeniera; Argentina.
Fil: Pastore, Juan Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ingeniera; CONICET; Argentina.
Peer Reviewed
Materia
—smoke detection; real time; embedded systems; video processing; image representation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2023-06-22T13:40:07Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/8087
Acceder
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Smoke detection using simplified descriptors of video informationMonte, GustavoMarasco, DamianPastore, Juan IgnacioLiscovsky, PabloBallarin, Virginia—smoke detection; real time; embedded systems; video processing; image representationAutomatic visual detection of smoke in confined or open spaces is overriding to issue early warnings that can save lives or prevent irreparable damage. While fire presents a range of characteristic colour, smoke does not present a readily apparent pattern. Changes its shape, does not contain clear edges, presents a chaotic behaviour and colour manifests from white to black, including all nuances. This paper presents an algorithm that efficiently pre-process a frame that extracts the main component of information, decreasing orders of magnitude the source size. From this new structure, algorithms based on the temporal and spatial change of subsets of the new structure are applied. Decision is based on fusion of weak classifiers. The algorithms are described and validated with experimental results of real-time detection for open and confined spaces, considering simplicity and efficiency of the proposed method suitable for embedded systems.Fil: Monte, Gustavo. Universidad Tecnológica Nacional. Facultad Regional Del Neuquen ; Argentina.Fil: Marasc, Damian. Universidad Tecnológica Nacional. Facultad Regional Del Neuquen ; Argentina.Fil: Liscovsky, Pablo. Universidad Tecnológica Nacional. Facultad Regional Del Neuquen ; Argentina.Fil: Ballarin, Virginia. Universidad Nacional de Mar del Plata. Facultad de Ingeniera; Argentina.Fil: Pastore, Juan Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ingeniera; CONICET; Argentina.Peer Reviewed2023-06-22T13:40:07Z2023-06-22T13:40:07Z2017-03-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfhttp://hdl.handle.net/20.500.12272/8087101109engenginfo:eu-repo/semantics/openAccess2023-06-22T13:40:07Zhttp://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalcreative commosreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:45:55Zoai:ria.utn.edu.ar:20.500.12272/8087instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:45:55.733Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Smoke detection using simplified descriptors of video information
title Smoke detection using simplified descriptors of video information
spellingShingle Smoke detection using simplified descriptors of video information
Monte, Gustavo
—smoke detection; real time; embedded systems; video processing; image representation
title_short Smoke detection using simplified descriptors of video information
title_full Smoke detection using simplified descriptors of video information
title_fullStr Smoke detection using simplified descriptors of video information
title_full_unstemmed Smoke detection using simplified descriptors of video information
title_sort Smoke detection using simplified descriptors of video information
dc.creator.none.fl_str_mv Monte, Gustavo
Marasco, Damian
Pastore, Juan Ignacio
Liscovsky, Pablo
Ballarin, Virginia
author Monte, Gustavo
author_facet Monte, Gustavo
Marasco, Damian
Pastore, Juan Ignacio
Liscovsky, Pablo
Ballarin, Virginia
author_role author
author2 Marasco, Damian
Pastore, Juan Ignacio
Liscovsky, Pablo
Ballarin, Virginia
author2_role author
author
author
author
dc.subject.none.fl_str_mv —smoke detection; real time; embedded systems; video processing; image representation
topic —smoke detection; real time; embedded systems; video processing; image representation
dc.description.none.fl_txt_mv Automatic visual detection of smoke in confined or open spaces is overriding to issue early warnings that can save lives or prevent irreparable damage. While fire presents a range of characteristic colour, smoke does not present a readily apparent pattern. Changes its shape, does not contain clear edges, presents a chaotic behaviour and colour manifests from white to black, including all nuances. This paper presents an algorithm that efficiently pre-process a frame that extracts the main component of information, decreasing orders of magnitude the source size. From this new structure, algorithms based on the temporal and spatial change of subsets of the new structure are applied. Decision is based on fusion of weak classifiers. The algorithms are described and validated with experimental results of real-time detection for open and confined spaces, considering simplicity and efficiency of the proposed method suitable for embedded systems.
Fil: Monte, Gustavo. Universidad Tecnológica Nacional. Facultad Regional Del Neuquen ; Argentina.
Fil: Marasc, Damian. Universidad Tecnológica Nacional. Facultad Regional Del Neuquen ; Argentina.
Fil: Liscovsky, Pablo. Universidad Tecnológica Nacional. Facultad Regional Del Neuquen ; Argentina.
Fil: Ballarin, Virginia. Universidad Nacional de Mar del Plata. Facultad de Ingeniera; Argentina.
Fil: Pastore, Juan Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ingeniera; CONICET; Argentina.
Peer Reviewed
description Automatic visual detection of smoke in confined or open spaces is overriding to issue early warnings that can save lives or prevent irreparable damage. While fire presents a range of characteristic colour, smoke does not present a readily apparent pattern. Changes its shape, does not contain clear edges, presents a chaotic behaviour and colour manifests from white to black, including all nuances. This paper presents an algorithm that efficiently pre-process a frame that extracts the main component of information, decreasing orders of magnitude the source size. From this new structure, algorithms based on the temporal and spatial change of subsets of the new structure are applied. Decision is based on fusion of weak classifiers. The algorithms are described and validated with experimental results of real-time detection for open and confined spaces, considering simplicity and efficiency of the proposed method suitable for embedded systems.
publishDate 2017
dc.date.none.fl_str_mv 2017-03-22
2023-06-22T13:40:07Z
2023-06-22T13:40:07Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12272/8087
101109
url http://hdl.handle.net/20.500.12272/8087
identifier_str_mv 101109
dc.language.none.fl_str_mv eng
eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2023-06-22T13:40:07Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
creative commos
eu_rights_str_mv openAccess
rights_invalid_str_mv 2023-06-22T13:40:07Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
creative commos
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.24418

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