Challenging situations for background subtraction algorithms
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808129521304469504 |