A cost-effective background subtraction technique.
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do ITA |
Texto Completo: | http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=596 |
Resumo: | Background Subtraction is a very important task in image processing because its results are used in algorithms that recognize more complex object behaviors. This proposed research technique extracts movement evidences from difference: 1) between two consecutive frames; 2) between current frame and the fourth previous frame; and 3) between the current frame and a background model. These evidences are combined using the strategy of adding complementary values before applying thresholds. This strategy, combined with the application of the "iterate only once" requirement, leads to a Cost-effective Background Subtraction Technique. The main contribution of this work is the development of a novel pixel classification metric. Besides, it was extended by the following incremental improvements: 1st) The proposition of a half-connected filter as a fullfilment of the "iterate only once" requirement; 2nd) The extension of a simple and efficient shadow filter; and 3rd) The development of a quick way to evaluate accuracy of background subtraction techniques, based on a Genetic Algorithm (GA) and a Distributed Processing environment. When compared to recent research, the proposed technique results are better in performance and accuracy, this last one is due to an optmization process using a Genetic Algorithm. When performing tests on an Intel Dual Core Pentium 1.60GHz microprocessor with 1GB RAM, up to 376 Frames Per Second (FPS) of 160x120 color images were classified using this technique. |
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A cost-effective background subtraction technique.Processamento de imagensTécnicas de formação de imagensMétricas (software)Algoritmos genéticosProcessamento distribuídoEngenharia eletrônicaBackground Subtraction is a very important task in image processing because its results are used in algorithms that recognize more complex object behaviors. This proposed research technique extracts movement evidences from difference: 1) between two consecutive frames; 2) between current frame and the fourth previous frame; and 3) between the current frame and a background model. These evidences are combined using the strategy of adding complementary values before applying thresholds. This strategy, combined with the application of the "iterate only once" requirement, leads to a Cost-effective Background Subtraction Technique. The main contribution of this work is the development of a novel pixel classification metric. Besides, it was extended by the following incremental improvements: 1st) The proposition of a half-connected filter as a fullfilment of the "iterate only once" requirement; 2nd) The extension of a simple and efficient shadow filter; and 3rd) The development of a quick way to evaluate accuracy of background subtraction techniques, based on a Genetic Algorithm (GA) and a Distributed Processing environment. When compared to recent research, the proposed technique results are better in performance and accuracy, this last one is due to an optmization process using a Genetic Algorithm. When performing tests on an Intel Dual Core Pentium 1.60GHz microprocessor with 1GB RAM, up to 376 Frames Per Second (FPS) of 160x120 color images were classified using this technique.Instituto Tecnológico de AeronáuticaOsamu SaotomeAlex Lopes Pereira2008-09-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=596reponame:Biblioteca Digital de Teses e Dissertações do ITAinstname:Instituto Tecnológico de Aeronáuticainstacron:ITAenginfo:eu-repo/semantics/openAccessapplication/pdf2019-02-02T14:01:50Zoai:agregador.ibict.br.BDTD_ITA:oai:ita.br:596http://oai.bdtd.ibict.br/requestopendoar:null2020-05-28 19:33:38.973Biblioteca Digital de Teses e Dissertações do ITA - Instituto Tecnológico de Aeronáuticatrue |
dc.title.none.fl_str_mv |
A cost-effective background subtraction technique. |
title |
A cost-effective background subtraction technique. |
spellingShingle |
A cost-effective background subtraction technique. Alex Lopes Pereira Processamento de imagens Técnicas de formação de imagens Métricas (software) Algoritmos genéticos Processamento distribuído Engenharia eletrônica |
title_short |
A cost-effective background subtraction technique. |
title_full |
A cost-effective background subtraction technique. |
title_fullStr |
A cost-effective background subtraction technique. |
title_full_unstemmed |
A cost-effective background subtraction technique. |
title_sort |
A cost-effective background subtraction technique. |
author |
Alex Lopes Pereira |
author_facet |
Alex Lopes Pereira |
author_role |
author |
dc.contributor.none.fl_str_mv |
Osamu Saotome |
dc.contributor.author.fl_str_mv |
Alex Lopes Pereira |
dc.subject.por.fl_str_mv |
Processamento de imagens Técnicas de formação de imagens Métricas (software) Algoritmos genéticos Processamento distribuído Engenharia eletrônica |
topic |
Processamento de imagens Técnicas de formação de imagens Métricas (software) Algoritmos genéticos Processamento distribuído Engenharia eletrônica |
dc.description.none.fl_txt_mv |
Background Subtraction is a very important task in image processing because its results are used in algorithms that recognize more complex object behaviors. This proposed research technique extracts movement evidences from difference: 1) between two consecutive frames; 2) between current frame and the fourth previous frame; and 3) between the current frame and a background model. These evidences are combined using the strategy of adding complementary values before applying thresholds. This strategy, combined with the application of the "iterate only once" requirement, leads to a Cost-effective Background Subtraction Technique. The main contribution of this work is the development of a novel pixel classification metric. Besides, it was extended by the following incremental improvements: 1st) The proposition of a half-connected filter as a fullfilment of the "iterate only once" requirement; 2nd) The extension of a simple and efficient shadow filter; and 3rd) The development of a quick way to evaluate accuracy of background subtraction techniques, based on a Genetic Algorithm (GA) and a Distributed Processing environment. When compared to recent research, the proposed technique results are better in performance and accuracy, this last one is due to an optmization process using a Genetic Algorithm. When performing tests on an Intel Dual Core Pentium 1.60GHz microprocessor with 1GB RAM, up to 376 Frames Per Second (FPS) of 160x120 color images were classified using this technique. |
description |
Background Subtraction is a very important task in image processing because its results are used in algorithms that recognize more complex object behaviors. This proposed research technique extracts movement evidences from difference: 1) between two consecutive frames; 2) between current frame and the fourth previous frame; and 3) between the current frame and a background model. These evidences are combined using the strategy of adding complementary values before applying thresholds. This strategy, combined with the application of the "iterate only once" requirement, leads to a Cost-effective Background Subtraction Technique. The main contribution of this work is the development of a novel pixel classification metric. Besides, it was extended by the following incremental improvements: 1st) The proposition of a half-connected filter as a fullfilment of the "iterate only once" requirement; 2nd) The extension of a simple and efficient shadow filter; and 3rd) The development of a quick way to evaluate accuracy of background subtraction techniques, based on a Genetic Algorithm (GA) and a Distributed Processing environment. When compared to recent research, the proposed technique results are better in performance and accuracy, this last one is due to an optmization process using a Genetic Algorithm. When performing tests on an Intel Dual Core Pentium 1.60GHz microprocessor with 1GB RAM, up to 376 Frames Per Second (FPS) of 160x120 color images were classified using this technique. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-09-11 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/masterThesis |
status_str |
publishedVersion |
format |
masterThesis |
dc.identifier.uri.fl_str_mv |
http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=596 |
url |
http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=596 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Instituto Tecnológico de Aeronáutica |
publisher.none.fl_str_mv |
Instituto Tecnológico de Aeronáutica |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do ITA instname:Instituto Tecnológico de Aeronáutica instacron:ITA |
reponame_str |
Biblioteca Digital de Teses e Dissertações do ITA |
collection |
Biblioteca Digital de Teses e Dissertações do ITA |
instname_str |
Instituto Tecnológico de Aeronáutica |
instacron_str |
ITA |
institution |
ITA |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações do ITA - Instituto Tecnológico de Aeronáutica |
repository.mail.fl_str_mv |
|
subject_por_txtF_mv |
Processamento de imagens Técnicas de formação de imagens Métricas (software) Algoritmos genéticos Processamento distribuído Engenharia eletrônica |
_version_ |
1706809260179980288 |