Challenging situations for background subtraction algorithms

Detalhes bibliográficos
Autor(a) principal: Sanches, Silvio R. R.
Data de Publicação: 2019
Outros Autores: Oliveira, Claiton, Sementille, Antonio C. [UNESP], Freire, Valdinei
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s10489-018-1346-4
http://hdl.handle.net/11449/184438
Resumo: Background subtraction is the prerequisite for a wide range of applications including video surveillance, smart environments and content retrieval. Real environments present some challenging situations even for the most recent algorithms, such as shadows, illumination changes, dynamic background, among others. If a real environment is previously known and the challenging situations of this environment can be predicted, the choice of an appropriate algorithm to deal with such situations may be essential for obtaining better segmentation results. In our work, we identify the main situations that affect the performance of background subtraction algorithms and present a classification of these challenging situations. In addition, we present a solution that uses videos and ground-truths from existing datasets to evaluate the performance of segmentation algorithms when they need to deal with a specific challenging situation.
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spelling Challenging situations for background subtraction algorithmsBackground subtractionForeground extractionAlgorithm evaluationChallenging situationBackground subtraction is the prerequisite for a wide range of applications including video surveillance, smart environments and content retrieval. Real environments present some challenging situations even for the most recent algorithms, such as shadows, illumination changes, dynamic background, among others. If a real environment is previously known and the challenging situations of this environment can be predicted, the choice of an appropriate algorithm to deal with such situations may be essential for obtaining better segmentation results. In our work, we identify the main situations that affect the performance of background subtraction algorithms and present a classification of these challenging situations. In addition, we present a solution that uses videos and ground-truths from existing datasets to evaluate the performance of segmentation algorithms when they need to deal with a specific challenging situation.Univ Tecnol Fed Parana, Cornelio Procopio, PR, BrazilUniv Estadual Paulista, Bauru, BrazilUniv Sao Paulo, Sao Paulo, BrazilUniv Estadual Paulista, Bauru, BrazilSpringerUniv Tecnol Fed ParanaUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Sanches, Silvio R. R.Oliveira, ClaitonSementille, Antonio C. [UNESP]Freire, Valdinei2019-10-04T12:13:38Z2019-10-04T12:13:38Z2019-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1771-1784http://dx.doi.org/10.1007/s10489-018-1346-4Applied Intelligence. Dordrecht: Springer, v. 49, n. 5, p. 1771-1784, 2019.0924-669Xhttp://hdl.handle.net/11449/18443810.1007/s10489-018-1346-4WOS:000463843400009Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Intelligenceinfo:eu-repo/semantics/openAccess2021-10-23T01:35:38Zoai:repositorio.unesp.br:11449/184438Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:26:40.370107Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Challenging situations for background subtraction algorithms
title Challenging situations for background subtraction algorithms
spellingShingle Challenging situations for background subtraction algorithms
Sanches, Silvio R. R.
Background subtraction
Foreground extraction
Algorithm evaluation
Challenging situation
title_short Challenging situations for background subtraction algorithms
title_full Challenging situations for background subtraction algorithms
title_fullStr Challenging situations for background subtraction algorithms
title_full_unstemmed Challenging situations for background subtraction algorithms
title_sort Challenging situations for background subtraction algorithms
author Sanches, Silvio R. R.
author_facet Sanches, Silvio R. R.
Oliveira, Claiton
Sementille, Antonio C. [UNESP]
Freire, Valdinei
author_role author
author2 Oliveira, Claiton
Sementille, Antonio C. [UNESP]
Freire, Valdinei
author2_role author
author
author
dc.contributor.none.fl_str_mv Univ Tecnol Fed Parana
Universidade Estadual Paulista (Unesp)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv Sanches, Silvio R. R.
Oliveira, Claiton
Sementille, Antonio C. [UNESP]
Freire, Valdinei
dc.subject.por.fl_str_mv Background subtraction
Foreground extraction
Algorithm evaluation
Challenging situation
topic Background subtraction
Foreground extraction
Algorithm evaluation
Challenging situation
description Background subtraction is the prerequisite for a wide range of applications including video surveillance, smart environments and content retrieval. Real environments present some challenging situations even for the most recent algorithms, such as shadows, illumination changes, dynamic background, among others. If a real environment is previously known and the challenging situations of this environment can be predicted, the choice of an appropriate algorithm to deal with such situations may be essential for obtaining better segmentation results. In our work, we identify the main situations that affect the performance of background subtraction algorithms and present a classification of these challenging situations. In addition, we present a solution that uses videos and ground-truths from existing datasets to evaluate the performance of segmentation algorithms when they need to deal with a specific challenging situation.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-04T12:13:38Z
2019-10-04T12:13:38Z
2019-05-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/s10489-018-1346-4
Applied Intelligence. Dordrecht: Springer, v. 49, n. 5, p. 1771-1784, 2019.
0924-669X
http://hdl.handle.net/11449/184438
10.1007/s10489-018-1346-4
WOS:000463843400009
url http://dx.doi.org/10.1007/s10489-018-1346-4
http://hdl.handle.net/11449/184438
identifier_str_mv Applied Intelligence. Dordrecht: Springer, v. 49, n. 5, p. 1771-1784, 2019.
0924-669X
10.1007/s10489-018-1346-4
WOS:000463843400009
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Applied Intelligence
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1771-1784
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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