Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da Uninove |
Texto Completo: | http://bibliotecatede.uninove.br/handle/tede/3137 |
Resumo: | The growth of the population and the fleet of vehicles all over the world, particularly in the big cities, have caused complications in terms of traffic. Solutions for reducing congestion and improving vehicle trafficability require effort and time, which is expensive and ineffective in the medium and long term considering Brazilian municipal administrations. This situation stems mainly from the need for adequate methodologies for efficient monitoring and data collection that public managers can analyze to serve as a basis for road and traffic infrastructure policies. Intelligent traffic monitoring is a new branch of intelligent transport systems that focuses on improving urban traffic conditions. The main objective of systems based on intelligent monitoring is to improve the traffic system, reduce vehicle fluidity problems and improve urban mobility. Many Brazilian cities face congestion due to the inefficient configuration of traffic lights, mainly based on fixed-cycle protocols. Within this context, there is a need to improve and automate the existing traffic light system. A mixed technique of artificial intelligence and computer vision may be desirable to develop a reliable and scalable traffic system that can help solve these problems. It first works in computer vision date back to the late 1970s, when computers could already process large data sets, such as images. In-depth studies in this area begin, and the field of computer vision can be characterized as immature and diverse, even after there are recognized works. Within this context, this dissertation focused on using computer vision technology to build an efficient and resourceful system for synchronous and automated traffic analysis. The performance of different algorithms and systems that apply computer vision to identify and track objects in urban traffic is discussed, detecting pedestrians and vehicles. With this, determining which algorithms and systems would best fit a more complex system based on IoT devices or even autonomous vehicles, which use computer vision systems. Somebody, Systematic Literature Review (SLR) were carried out in articles published from 01/01/1970 to 01/31/2023. As BGS is a technique used in computer vision to detect moving objects in a sequence of images obtained from a video, for example, it was found that relying only on BGS without any other processing or machine learning (ML) support or deep learning (DL), are not accurate enough for direct application in traffic light systems or autonomous monitoring. However, with the constant evolution of specialized software and hardware, the joint application of these solutions is promising. As the results of this work demonstrate, there is a need to improve the individual filters and, in this way, try to make them more reliable when applicable and implement them aided by other technologies, in an attempt to become more assertive in terms of precision and other metrics, such as false positives and true positives. |
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Paschoalin Filho, João Alexandrehttp://lattes.cnpq.br/7933402277545891Paschoalin Filho, João Alexandrehttp://lattes.cnpq.br/7933402277545891Dias, Cleber Gustavohttp://lattes.cnpq.br/2147386441758156Quaresma, Cristiano Capellanihttp://lattes.cnpq.br/9287861770521337Barboza, Rogério Santino2023-05-09T17:23:46Z2023-02-24Barboza, Rogério Santino. Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres. 2023. 280 f. Dissertação( Programa de Pós-Graduação em Cidades Inteligentes e Sustentáveis) - Universidade Nove de Julho, São Paulo.http://bibliotecatede.uninove.br/handle/tede/3137The growth of the population and the fleet of vehicles all over the world, particularly in the big cities, have caused complications in terms of traffic. Solutions for reducing congestion and improving vehicle trafficability require effort and time, which is expensive and ineffective in the medium and long term considering Brazilian municipal administrations. This situation stems mainly from the need for adequate methodologies for efficient monitoring and data collection that public managers can analyze to serve as a basis for road and traffic infrastructure policies. Intelligent traffic monitoring is a new branch of intelligent transport systems that focuses on improving urban traffic conditions. The main objective of systems based on intelligent monitoring is to improve the traffic system, reduce vehicle fluidity problems and improve urban mobility. Many Brazilian cities face congestion due to the inefficient configuration of traffic lights, mainly based on fixed-cycle protocols. Within this context, there is a need to improve and automate the existing traffic light system. A mixed technique of artificial intelligence and computer vision may be desirable to develop a reliable and scalable traffic system that can help solve these problems. It first works in computer vision date back to the late 1970s, when computers could already process large data sets, such as images. In-depth studies in this area begin, and the field of computer vision can be characterized as immature and diverse, even after there are recognized works. Within this context, this dissertation focused on using computer vision technology to build an efficient and resourceful system for synchronous and automated traffic analysis. The performance of different algorithms and systems that apply computer vision to identify and track objects in urban traffic is discussed, detecting pedestrians and vehicles. With this, determining which algorithms and systems would best fit a more complex system based on IoT devices or even autonomous vehicles, which use computer vision systems. Somebody, Systematic Literature Review (SLR) were carried out in articles published from 01/01/1970 to 01/31/2023. As BGS is a technique used in computer vision to detect moving objects in a sequence of images obtained from a video, for example, it was found that relying only on BGS without any other processing or machine learning (ML) support or deep learning (DL), are not accurate enough for direct application in traffic light systems or autonomous monitoring. However, with the constant evolution of specialized software and hardware, the joint application of these solutions is promising. As the results of this work demonstrate, there is a need to improve the individual filters and, in this way, try to make them more reliable when applicable and implement them aided by other technologies, in an attempt to become more assertive in terms of precision and other metrics, such as false positives and true positives.O crescimento da população e da frota de veículos em todo o mundo, principalmente nas grandes cidades, provoca o aumento dos congestionamentos, gerando complicações no trânsito. As principais soluções para reduzir o congestionamento requerem tempo, que é caro e ineficaz a médio e longo prazo. O monitoramento inteligente de tráfego é um ramo dos sistemas de transporte inteligente que se concentra em melhorar as condições do tráfego urbano. O principal objetivo de tais sistemas é melhorar o sistema de tráfego, reduzindo os problemas de fluidez dos veículos e melhorando a mobilidade urbana. Neste contexto, surge a necessidade de melhorar e automatizar o sistema de semáforos existente. Estabelecer uma técnica mista de inteligência artificial e visão computacional pode ser desejável para desenvolver um sistema de tráfego confiável e escalável que possa ajudar a resolver esses problemas. Este trabalho tem como foco a utilização da tecnologia de visão computacional como ferramenta para a construção de um sistema com recursos para análise de tráfego síncrona e automatizada, para determinar qual algoritmo a ser aplicado, é comparado o desempenho de diferentes algoritmos de subtração de fundo (BGS) em um sistema prático que aplica visão computacional para identificar e rastrear veículos e ser capaz de detectar pedestres é apresentado. O software foi gerado para analisar algoritmos BGS, conta ainda, com contagem de veículos, registro de veículos e sentido de direção, divididos em dois grupos: carro e caminhão. Em outro software, um aplicativo específico para pedestres. Com isso, foi possível determinar quais algoritmos podem ser mais efetivamente adequados a um sistema mais complexo, como o gerenciamento de tráfego urbano, por meio da adoção de dispositivos IoT na borda ou em veículos autônomos que utilizam de alguma forma sistemas de visão computacional — uma interface gráfica foi disponibilizada para auxiliar no estudo destes algoritmos. A pesquisa tem como objetivo através da Revisão Sistemática da Literatura (RSL) de artigos publicados no período de 01/01/1970 a 31/01/2023, através de trabalhos relacionados ao monitoramento do tráfego urbano de pedestres e veículos diversos . Como o BGS é uma técnica utilizada em visão computacional para detectar objetos em movimento em uma sequência de imagens obtidas de um vídeo, por exemplo, verificou-se que contar apenas com o BGS sem nenhum outro processamento ou suporte por aprendizado de máquina (ML) ou deep learning (DL), não apresentam acurácia o suficiente para aplicação direta em sistemas de semáforos ou monitoramento autônomo. Porém, com a constante evolução de softwares e hardwares especializados, a aplicação conjunta dessas soluções é promissora. Como demonstram os resultados deste trabalho, existe a necessidade de melhorar os filtros individuais e desta forma, procurar deixá-los mais confiáveis quando aplicáveis e implementá-los auxiliados por outras tecnologias, na tentativa de se tornar mais assertivos em termos de precisão e de outras métricas, como por exemplo: falsos positivos e verdadeiros positivos.Submitted by Nadir Basilio (nadirsb@uninove.br) on 2023-05-09T17:23:46Z No. of bitstreams: 1 Rogério Santino Barboza.pdf: 23266980 bytes, checksum: abcc19091052724b0a0aafa3c915d9fe (MD5)Made available in DSpace on 2023-05-09T17:23:46Z (GMT). No. of bitstreams: 1 Rogério Santino Barboza.pdf: 23266980 bytes, checksum: abcc19091052724b0a0aafa3c915d9fe (MD5) Previous issue date: 2023-02-24application/pdfporUniversidade Nove de JulhoPrograma de Pós-Graduação em Cidades Inteligentes e SustentáveisUNINOVEBrasilAdministraçãovisão computacionaldetecção de objetosOpenCVcidades inteligentesalgoritmos de subtração de fundocomputer visionobject detectionOpenCVsmart citiesbackground subtraction algorithmsCIENCIAS SOCIAIS APLICADAS::ADMINISTRACAOVisão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestresComputer vision: comparative study between background subtraction algorithms applied in solutions for urban traffic management of vehicles and pedestriansinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis8024035432632778221600info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da Uninoveinstname:Universidade Nove de Julho (UNINOVE)instacron:UNINOVEORIGINALRogério Santino Barboza.pdfRogério Santino Barboza.pdfapplication/pdf23266980http://localhost:8080/tede/bitstream/tede/3137/2/Rog%C3%A9rio+Santino+Barboza.pdfabcc19091052724b0a0aafa3c915d9feMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://localhost:8080/tede/bitstream/tede/3137/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede/31372023-05-09 14:23:46.32oai:localhost: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Biblioteca Digital de Teses e Dissertaçõeshttp://bibliotecatede.uninove.br/PRIhttp://bibliotecatede.uninove.br/oai/requestbibliotecatede@uninove.br||bibliotecatede@uninove.bropendoar:2023-05-09T17:23:46Biblioteca Digital de Teses e Dissertações da Uninove - Universidade Nove de Julho (UNINOVE)false |
dc.title.por.fl_str_mv |
Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres |
dc.title.alternative.eng.fl_str_mv |
Computer vision: comparative study between background subtraction algorithms applied in solutions for urban traffic management of vehicles and pedestrians |
title |
Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres |
spellingShingle |
Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres Barboza, Rogério Santino visão computacional detecção de objetos OpenCV cidades inteligentes algoritmos de subtração de fundo computer vision object detection OpenCV smart cities background subtraction algorithms CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO |
title_short |
Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres |
title_full |
Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres |
title_fullStr |
Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres |
title_full_unstemmed |
Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres |
title_sort |
Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres |
author |
Barboza, Rogério Santino |
author_facet |
Barboza, Rogério Santino |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Paschoalin Filho, João Alexandre |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/7933402277545891 |
dc.contributor.referee1.fl_str_mv |
Paschoalin Filho, João Alexandre |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/7933402277545891 |
dc.contributor.referee2.fl_str_mv |
Dias, Cleber Gustavo |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/2147386441758156 |
dc.contributor.referee3.fl_str_mv |
Quaresma, Cristiano Capellani |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/9287861770521337 |
dc.contributor.author.fl_str_mv |
Barboza, Rogério Santino |
contributor_str_mv |
Paschoalin Filho, João Alexandre Paschoalin Filho, João Alexandre Dias, Cleber Gustavo Quaresma, Cristiano Capellani |
dc.subject.por.fl_str_mv |
visão computacional detecção de objetos OpenCV cidades inteligentes algoritmos de subtração de fundo |
topic |
visão computacional detecção de objetos OpenCV cidades inteligentes algoritmos de subtração de fundo computer vision object detection OpenCV smart cities background subtraction algorithms CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO |
dc.subject.eng.fl_str_mv |
computer vision object detection OpenCV smart cities background subtraction algorithms |
dc.subject.cnpq.fl_str_mv |
CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO |
description |
The growth of the population and the fleet of vehicles all over the world, particularly in the big cities, have caused complications in terms of traffic. Solutions for reducing congestion and improving vehicle trafficability require effort and time, which is expensive and ineffective in the medium and long term considering Brazilian municipal administrations. This situation stems mainly from the need for adequate methodologies for efficient monitoring and data collection that public managers can analyze to serve as a basis for road and traffic infrastructure policies. Intelligent traffic monitoring is a new branch of intelligent transport systems that focuses on improving urban traffic conditions. The main objective of systems based on intelligent monitoring is to improve the traffic system, reduce vehicle fluidity problems and improve urban mobility. Many Brazilian cities face congestion due to the inefficient configuration of traffic lights, mainly based on fixed-cycle protocols. Within this context, there is a need to improve and automate the existing traffic light system. A mixed technique of artificial intelligence and computer vision may be desirable to develop a reliable and scalable traffic system that can help solve these problems. It first works in computer vision date back to the late 1970s, when computers could already process large data sets, such as images. In-depth studies in this area begin, and the field of computer vision can be characterized as immature and diverse, even after there are recognized works. Within this context, this dissertation focused on using computer vision technology to build an efficient and resourceful system for synchronous and automated traffic analysis. The performance of different algorithms and systems that apply computer vision to identify and track objects in urban traffic is discussed, detecting pedestrians and vehicles. With this, determining which algorithms and systems would best fit a more complex system based on IoT devices or even autonomous vehicles, which use computer vision systems. Somebody, Systematic Literature Review (SLR) were carried out in articles published from 01/01/1970 to 01/31/2023. As BGS is a technique used in computer vision to detect moving objects in a sequence of images obtained from a video, for example, it was found that relying only on BGS without any other processing or machine learning (ML) support or deep learning (DL), are not accurate enough for direct application in traffic light systems or autonomous monitoring. However, with the constant evolution of specialized software and hardware, the joint application of these solutions is promising. As the results of this work demonstrate, there is a need to improve the individual filters and, in this way, try to make them more reliable when applicable and implement them aided by other technologies, in an attempt to become more assertive in terms of precision and other metrics, such as false positives and true positives. |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-05-09T17:23:46Z |
dc.date.issued.fl_str_mv |
2023-02-24 |
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info:eu-repo/semantics/publishedVersion |
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Barboza, Rogério Santino. Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres. 2023. 280 f. Dissertação( Programa de Pós-Graduação em Cidades Inteligentes e Sustentáveis) - Universidade Nove de Julho, São Paulo. |
dc.identifier.uri.fl_str_mv |
http://bibliotecatede.uninove.br/handle/tede/3137 |
identifier_str_mv |
Barboza, Rogério Santino. Visão computacional: estudo comparativo de algoritmos de subtração de fundo aplicados em soluções para o gerenciamento de tráfego urbano de veículos e pedestres. 2023. 280 f. Dissertação( Programa de Pós-Graduação em Cidades Inteligentes e Sustentáveis) - Universidade Nove de Julho, São Paulo. |
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