Please use this identifier to cite or link to this item: https://repositorio.ufu.br/handle/123456789/27115
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dc.creatorSilva Junior, Gilson Mendes da-
dc.date.accessioned2019-10-08T17:34:36Z-
dc.date.available2019-10-08T17:34:36Z-
dc.date.issued2019-09-29-
dc.identifier.citationSILVA JUNIOR, Gilson Mendes da. Similarity-based techniques for visual analysis of surveillance video. 2019. 90 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Uberlândia, Uberlândia, 2019. Disponível em https://dx.doi.org/10.14393/ufu.di.2019.67.pt_BR
dc.identifier.urihttps://repositorio.ufu.br/handle/123456789/27115-
dc.description.abstractSurveillance camera systems based on CCTV (closed-circuit television) are widely employed in a variety of society segments, from private and public security to crowd monitoring and terrorist attack prevention, generating a large volume of surveillance videos. The manual analysis of these videos is unfeasible due to the excessive amount of data to be analyzed, the associated subjectivity, and the presence of noise that can cause distraction and compromise the comprehension of relevant events, impairing an effective analysis. Automatic summarization techniques are usually employed to facilitate this analysis, providing additional information that may guide the security agent in this decision making. However, these strategies provide little/no user interaction, limiting his/her comprehension regarding the involved phenomena. Furthermore, such techniques only address specific scenarios, in the sense that no approach is good for all situations. In this sense, it is important to insert the user in the analysis process, as they provide the additional knowledge to effectively perform the events identification and exploration. Visual analytics techniques represent a potential tool for such analysis, providing video representations that clearly communicate their content, potentially revealing patterns that may represent events of interest. These representations can significantly increase the capacity of the security agent to identify important events, and filtering/exploring those that represent potential alert situations. In this project we propose a methodology for visual analysis of surveillance videos that employs Information Visualization techniques for events exploration. We specifically coordinate point-placement techniques and Temporal Self-similarity Maps (TSSMs) to create an analysis environment that reveal both structural and temporal aspects related to event occurrence. Users are able to interact with these layouts, in order to change the visualization perspective, focus on specific portions of the video, among other tasks. We present experiments in several surveillance scenarios that demonstrate the ability of the proposed methodology in providing an effective events summarization, the exploration of both the structure of each event and the relationship among them, as well as their temporal properties. The main contribution of this work is a surveillance visual analysis system which provides a deep exploration of different aspects present on surveillance videos regarding events occurrence, providing an effective analysis and a rapid decision making.pt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Uberlândiapt_BR
dc.rightsAcesso Abertopt_BR
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectComputaçãopt_BR
dc.subjectVigilância eletrônicapt_BR
dc.subjectVideovigilânciapt_BR
dc.subjectVisualização da informaçãopt_BR
dc.subjectSistemas de segurança - monitoramentopt_BR
dc.subjectVigilância inteligentept_BR
dc.subjectVisualização baseada em similaridadept_BR
dc.subjectDetecção de eventospt_BR
dc.subjectSmart surveillancept_BR
dc.subjectInformation visualizationpt_BR
dc.subjectSimilarity-based visualizationpt_BR
dc.subjectEvents detectionpt_BR
dc.titleTécnicas baseadas em similaridade para análise visual de videos de segurançapt_BR
dc.title.alternativeSimilarity-based techniques for Visual Analysis of Surveillance Videopt_BR
dc.typeDissertaçãopt_BR
dc.contributor.advisor1Paiva, José Gustavo de Souza-
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/4981210260282182pt_BR
dc.contributor.referee1Eler, Danilo Medeiros-
dc.contributor.referee1Latteshttp://lattes.cnpq.br/0840226903480590pt_BR
dc.contributor.referee2Travençolo, Bruno Augusto Nassif-
dc.contributor.referee2Latteshttp://lattes.cnpq.br/2590427557264952pt_BR
dc.creator.Latteshttp://lattes.cnpq.br/1461891992942263pt_BR
dc.description.degreenameDissertação (Mestrado)pt_BR
dc.description.resumoSurveillance camera systems based on CCTV (closed-circuit television) are widely employed in a variety of society segments, from private and public security to crowd monitoring and terrorist attack prevention, generating a large volume of surveillance videos. The manual analysis of these videos is unfeasible due to the excessive amount of data to be analyzed, the associated subjectivity, and the presence of noise that can cause distraction and compromise the comprehension of relevant events, impairing an effective analysis. Automatic summarization techniques are usually employed to facilitate this analysis, providing additional information that may guide the security agent in this decision making. However, these strategies provide little/no user interaction, limiting his/her comprehension regarding the involved phenomena. Furthermore, such techniques only address specific scenarios, in the sense that no approach is good for all situations. In this sense, it is important to insert the user in the analysis process, as they provide the additional knowledge to effectively perform the events identification and exploration. Visual analytics techniques represent a potential tool for such analysis, providing video representations that clearly communicate their content, potentially revealing patterns that may represent events of interest. These representations can significantly increase the capacity of the security agent to identify important events, and filtering/exploring those that represent potential alert situations. In this project we propose a methodology for visual analysis of surveillance videos that employs Information Visualization techniques for events exploration. We specifically coordinate point-placement techniques and Temporal Self-similarity Maps (TSSMs) to create an analysis environment that reveal both structural and temporal aspects related to event occurrence. Users are able to interact with these layouts, in order to change the visualization perspective, focus on specific portions of the video, among other tasks. We present experiments in several surveillance scenarios that demonstrate the ability of the proposed methodology in providing an effective events summarization, the exploration of both the structure of each event and the relationship among them, as well as their temporal properties. The main contribution of this work is a surveillance visual analysis system which provides a deep exploration of different aspects present on surveillance videos regarding events occurrence, providing an effective analysis and a rapid decision making.pt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.programPrograma de Pós-graduação em Ciência da Computaçãopt_BR
dc.sizeorduration91pt_BR
dc.subject.cnpqCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::BANCO DE DADOSpt_BR
dc.subject.cnpqCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::SISTEMAS DE INFORMACAOpt_BR
dc.subject.cnpqCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::PROCESSAMENTO GRAFICO (GRAPHICS)pt_BR
dc.identifier.doihttp://dx.doi.org/10.14393/ufu.di.2019.67pt_BR
dc.crossref.doibatchidcfc6af78-95df-434f-8cba-ff3aa9588d23-
Appears in Collections:DISSERTAÇÃO - Ciência da Computação

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