Video Segmentation Learning Using Cascade Residual Convolutional Neural Network

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorPetr Brasileiro SA Petrobras-
Autor(es): dc.creatorSantos, Daniel F. S. [UNESP]-
Autor(es): dc.creatorPires, Rafael G. [UNESP]-
Autor(es): dc.creatorColombo, Danilo-
Autor(es): dc.creatorPapa, Joao P. [UNESP]-
Autor(es): dc.creatorIEEE-
Data de aceite: dc.date.accessioned2022-02-22T00:10:15Z-
Data de disponibilização: dc.date.available2022-02-22T00:10:15Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2019-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI.2019.00009-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/196721-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/196721-
Descrição: dc.descriptionVideo segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of 0.9535 and 0.9636 in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionPetrobras grant-
Descrição: dc.descriptionSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil-
Descrição: dc.descriptionPetr Brasileiro SA Petrobras, Cenpes, Rio De Janeiro, RJ, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil-
Descrição: dc.descriptionCNPq: 307066/2017-7-
Descrição: dc.descriptionCNPq: 427968/2018-6-
Descrição: dc.descriptionFAPESP: 2013/07375-0-
Descrição: dc.descriptionFAPESP: 2014/12236-1-
Descrição: dc.descriptionFAPESP: 2016/19403-6-
Descrição: dc.descriptionPetrobras grant: 2017/00285-6-
Formato: dc.format1-7-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation2019 32nd Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi)-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectVideo Segmentation-
Palavras-chave: dc.subjectDeep Learning-
Palavras-chave: dc.subjectForeground Object Detection-
Palavras-chave: dc.subjectResidual Map-
Título: dc.titleVideo Segmentation Learning Using Cascade Residual Convolutional Neural Network-
Aparece nas coleções:Repositório Institucional - Unesp

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