Robust approaches for anomaly detection applied to video surveillance

Detalhes bibliográficos
Autor(a) principal: Rensso Victor Hugo Mora Colque
Data de Publicação: 2018
Tipo de documento: Tese
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: http://hdl.handle.net/1843/32271
Resumo: CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
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spelling William Robson Schwartzhttp://lattes.cnpq.br/0704592200063682Guillermo Cámara ChavezErickson Rangel do NascimentoJefersson Alex dos SantosMoacir Antonelli PontiCláudio Rosito Junghttp://lattes.cnpq.br/3626581822560772Rensso Victor Hugo Mora Colque2020-01-28T16:41:12Z2020-01-28T16:41:12Z2018-08-24http://hdl.handle.net/1843/32271CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorModeling human behavior and activity patterns for detection of anomalous events has attracted significant research interest in recent years, particularly among the video surveillance community. An anomalous event might be characterized by the deviation from the normal or usual, but not necessarily in an undesirable manner. One of the main challenges of detecting such events is the difficulty to create models due to their unpredictability and their dependency on the context of the scene. Anomalous events detection or anomaly recognition for surveillance videos is a very hard problem. Since anomalous events depend on the characteristic or the context of a specific scene. Although many contexts could be similar, the events that can be considered anomalous are also infinity, i.e., cannot be learned beforehand. In this dissertation, we propose three approaches to detect anomalous patterns in surveillance video sequences. In the first approach, we present an approach based on a handcrafted feature descriptor that employs general concepts, such as orientation, velocity, and entropy to build a descriptor for spatiotemporal regions. With this histogram, we can compare them and detect anomalies in video sequences. The main advantage of this approach is its simplicity and promising results that will be show in the experimental results, where our descriptors had well performance in famous dataset as UCSD and Subway, reaching comparative results with the estate of the art, specially in UCSD peds2 view. This results show that this model fits well in scenes with crowds. In the second proposal, we develop an approach based on human-object interactions. This approach explores the scene context to determine normal patterns and finally detect whether some video segment contains a possible anomalous event. To validate this approach we proposed a novel dataset which contains anomalies based on the human object interactions, the results are promising, however, this approach must be extended to be robust to more situations and environments. In the third approach, we propose a novel method based on semantic information of people movement. While, most studies focus in information extracted from spatiotemporal regions, our approach detects anomalies based on human trajectory. The results show that our model is suitable to detect anomalies in environments where trajectory of the people could be extracted. The main difference among the proposed approaches is the source to describe the events in the scene. The first method intends to represent the scene from spatiotemporal regions, the second uses the human-object interactions and the third uses the people trajectory. Each approach is oriented to certain anomaly types, having advantages and disadvantages according to the inherit limitation of the source and to the subjective of normal and anomaly event definition in a determinate context.engUniversidade Federal de Minas GeraisPrograma de Pós-Graduação em Ciência da ComputaçãoUFMGBrasilICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃOComputação - Teses.Visão por computador - Teses.Processamento de Imagens- Teses.Sistemas eletrônicos de segurança.- Teses.Computação - TesesVisão por computadorProcessamento de ImagensSistemas eletrônicos de segurançaRobust approaches for anomaly detection applied to video surveillanceinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGORIGINALtese.pdftese.pdfapplication/pdf6281742https://repositorio.ufmg.br/bitstream/1843/32271/1/tese.pdfa6f7ecdd8679556f873f0d343bb8d766MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82119https://repositorio.ufmg.br/bitstream/1843/32271/2/license.txt34badce4be7e31e3adb4575ae96af679MD52TEXTtese.pdf.txttese.pdf.txtExtracted texttext/plain244716https://repositorio.ufmg.br/bitstream/1843/32271/3/tese.pdf.txt93a6d467437fdc367d881e71c5857032MD531843/322712020-01-29 03:32:26.195oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2020-01-29T06:32:26Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv Robust approaches for anomaly detection applied to video surveillance
title Robust approaches for anomaly detection applied to video surveillance
spellingShingle Robust approaches for anomaly detection applied to video surveillance
Rensso Victor Hugo Mora Colque
Computação - Teses
Visão por computador
Processamento de Imagens
Sistemas eletrônicos de segurança
Computação - Teses.
Visão por computador - Teses.
Processamento de Imagens- Teses.
Sistemas eletrônicos de segurança.- Teses.
title_short Robust approaches for anomaly detection applied to video surveillance
title_full Robust approaches for anomaly detection applied to video surveillance
title_fullStr Robust approaches for anomaly detection applied to video surveillance
title_full_unstemmed Robust approaches for anomaly detection applied to video surveillance
title_sort Robust approaches for anomaly detection applied to video surveillance
author Rensso Victor Hugo Mora Colque
author_facet Rensso Victor Hugo Mora Colque
author_role author
dc.contributor.advisor1.fl_str_mv William Robson Schwartz
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/0704592200063682
dc.contributor.advisor-co1.fl_str_mv Guillermo Cámara Chavez
dc.contributor.referee1.fl_str_mv Erickson Rangel do Nascimento
dc.contributor.referee2.fl_str_mv Jefersson Alex dos Santos
dc.contributor.referee3.fl_str_mv Moacir Antonelli Ponti
dc.contributor.referee4.fl_str_mv Cláudio Rosito Jung
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/3626581822560772
dc.contributor.author.fl_str_mv Rensso Victor Hugo Mora Colque
contributor_str_mv William Robson Schwartz
Guillermo Cámara Chavez
Erickson Rangel do Nascimento
Jefersson Alex dos Santos
Moacir Antonelli Ponti
Cláudio Rosito Jung
dc.subject.por.fl_str_mv Computação - Teses
Visão por computador
Processamento de Imagens
Sistemas eletrônicos de segurança
topic Computação - Teses
Visão por computador
Processamento de Imagens
Sistemas eletrônicos de segurança
Computação - Teses.
Visão por computador - Teses.
Processamento de Imagens- Teses.
Sistemas eletrônicos de segurança.- Teses.
dc.subject.other.pt_BR.fl_str_mv Computação - Teses.
Visão por computador - Teses.
Processamento de Imagens- Teses.
Sistemas eletrônicos de segurança.- Teses.
description CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
publishDate 2018
dc.date.issued.fl_str_mv 2018-08-24
dc.date.accessioned.fl_str_mv 2020-01-28T16:41:12Z
dc.date.available.fl_str_mv 2020-01-28T16:41:12Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1843/32271
url http://hdl.handle.net/1843/32271
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.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.program.fl_str_mv Programa de Pós-Graduação em Ciência da Computação
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
bitstream.url.fl_str_mv https://repositorio.ufmg.br/bitstream/1843/32271/1/tese.pdf
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