Exploiting multiple cues in motion segmentation based on background subtraction

This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motion segmentation. In our first contribution, a case analysis of motion segmentation errors is presented taking into acco...

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Detalhes bibliográficos
Autores: Huerta, Iván, Amato, Ariel, Roca, F. Xavier, Gonzàlez, Jordi
Formato: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2013
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/96387
Acesso em linha:http://hdl.handle.net/10261/96387
Access Level:acceso abierto
Palavra-chave:Motion segmentation
Shadow suppression
Background subtraction
Edge segmentation
Ghost detection
Colour segmentation
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repository_id_str
spelling Exploiting multiple cues in motion segmentation based on background subtractionHuerta, IvánAmato, ArielRoca, F. XavierGonzàlez, JordiMotion segmentationShadow suppressionBackground subtractionEdge segmentationGhost detectionColour segmentationThis paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motion segmentation. In our first contribution, a case analysis of motion segmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motion segmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches. © 2012 Elsevier B.V.This work has beensupported by the Spanish Research Programs Consolider-Ingenio 2010: MIPRCV (CSD200700018); Avanza I+D ViCoMo (TSI-020400-2009-133); along with the Spanish projects TIN2009-14501-C02-01,TIN2009-14501-C02-02, and DIP2010-17112.Peer ReviewedElsevierConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2014201420132014info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Preprintinfo:eu-repo/semantics/submittedVersionhttp://hdl.handle.net/10261/96387reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1016/j.neucom.2011.10.036Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/963872026-05-22T06:33:51Z
dc.title.none.fl_str_mv Exploiting multiple cues in motion segmentation based on background subtraction
title Exploiting multiple cues in motion segmentation based on background subtraction
spellingShingle Exploiting multiple cues in motion segmentation based on background subtraction
Huerta, Iván
Motion segmentation
Shadow suppression
Background subtraction
Edge segmentation
Ghost detection
Colour segmentation
title_short Exploiting multiple cues in motion segmentation based on background subtraction
title_full Exploiting multiple cues in motion segmentation based on background subtraction
title_fullStr Exploiting multiple cues in motion segmentation based on background subtraction
title_full_unstemmed Exploiting multiple cues in motion segmentation based on background subtraction
title_sort Exploiting multiple cues in motion segmentation based on background subtraction
dc.creator.none.fl_str_mv Huerta, Iván
Amato, Ariel
Roca, F. Xavier
Gonzàlez, Jordi
author Huerta, Iván
author_facet Huerta, Iván
Amato, Ariel
Roca, F. Xavier
Gonzàlez, Jordi
author_role author
author2 Amato, Ariel
Roca, F. Xavier
Gonzàlez, Jordi
author2_role author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Motion segmentation
Shadow suppression
Background subtraction
Edge segmentation
Ghost detection
Colour segmentation
topic Motion segmentation
Shadow suppression
Background subtraction
Edge segmentation
Ghost detection
Colour segmentation
description This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motion segmentation. In our first contribution, a case analysis of motion segmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motion segmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches. © 2012 Elsevier B.V.
publishDate 2013
dc.date.none.fl_str_mv 2013
2014
2014
2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Preprint
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/96387
url http://hdl.handle.net/10261/96387
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1016/j.neucom.2011.10.036
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
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score 15.81155

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