Vista Equipo: Real-time flood detection for video surveillance

Real-time flood detection for video surveillance

This paper introduces the real-time flash flood detection method for stationary surveillance cameras. It can be applied for rural and urban areas and capable of working during day time. The background subtraction was used to detect all changes appear in a scene. After this step, many pixel belonging...

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Autores Principales:	Cáceres Hernández, Danilo, Hyun Jo, Kang, Filonenko, Alexander, Seo, Dongwook
Formato:	Artículo
Idioma:	Inglés
Publicado:	2018
Materias:	Image color analysis Cameras Real-time systems Graphics processing units Probability Surveillance Floods
Acceso en línea:	https://ieeexplore.ieee.org/abstract/document/7392736/ http://ridda2.utp.ac.pa/handle/123456789/5091 http://ridda2.utp.ac.pa/handle/123456789/5091

id	RepoUTP5091
recordtype	dspace
spelling	RepoUTP50912021-07-06T15:34:54Z Real-time flood detection for video surveillance Cáceres Hernández, Danilo Hyun Jo, Kang Filonenko, Alexander Seo, Dongwook Image color analysis Cameras Real-time systems Graphics processing units Probability Surveillance Floods Image color analysis Cameras Real-time systems Graphics processing units Probability Surveillance Floods This paper introduces the real-time flash flood detection method for stationary surveillance cameras. It can be applied for rural and urban areas and capable of working during day time. The background subtraction was used to detect all changes appear in a scene. After this step, many pixel belonging to the same moving objects may be divided. They are united by morphological closing. Too small separate objects are then removed form the scene. Color probability was calculated for all the pixels belonging to a foreground mask and connected components with low probability value were filtered out. Finally, results were improved by edge density and boundary roughness. The most time consuming step was implemented in parallel using CUDA. Real-time performance was achieved in this way. The algorithm was tested on publicly accepted video. This paper introduces the real-time flash flood detection method for stationary surveillance cameras. It can be applied for rural and urban areas and capable of working during day time. The background subtraction was used to detect all changes appear in a scene. After this step, many pixel belonging to the same moving objects may be divided. They are united by morphological closing. Too small separate objects are then removed form the scene. Color probability was calculated for all the pixels belonging to a foreground mask and connected components with low probability value were filtered out. Finally, results were improved by edge density and boundary roughness. The most time consuming step was implemented in parallel using CUDA. Real-time performance was achieved in this way. The algorithm was tested on publicly accepted video. 2018-06-29T21:27:03Z 2018-06-29T21:27:03Z 2018-06-29T21:27:03Z 2018-06-29T21:27:03Z 11/09/2015 11/09/2015 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://ieeexplore.ieee.org/abstract/document/7392736/ http://ridda2.utp.ac.pa/handle/123456789/5091 http://ridda2.utp.ac.pa/handle/123456789/5091 eng info:eu-repo/semantics/embargoedAccess application/pdf text/html
institution	Universidad Tecnológica de Panamá
collection	Repositorio UTP – Ridda2
language	Inglés
topic	Image color analysis Cameras Real-time systems Graphics processing units Probability Surveillance Floods Image color analysis Cameras Real-time systems Graphics processing units Probability Surveillance Floods
spellingShingle	Image color analysis Cameras Real-time systems Graphics processing units Probability Surveillance Floods Image color analysis Cameras Real-time systems Graphics processing units Probability Surveillance Floods Cáceres Hernández, Danilo Hyun Jo, Kang Filonenko, Alexander Seo, Dongwook Real-time flood detection for video surveillance
description	This paper introduces the real-time flash flood detection method for stationary surveillance cameras. It can be applied for rural and urban areas and capable of working during day time. The background subtraction was used to detect all changes appear in a scene. After this step, many pixel belonging to the same moving objects may be divided. They are united by morphological closing. Too small separate objects are then removed form the scene. Color probability was calculated for all the pixels belonging to a foreground mask and connected components with low probability value were filtered out. Finally, results were improved by edge density and boundary roughness. The most time consuming step was implemented in parallel using CUDA. Real-time performance was achieved in this way. The algorithm was tested on publicly accepted video.
format	Artículo
author	Cáceres Hernández, Danilo Hyun Jo, Kang Filonenko, Alexander Seo, Dongwook
author_sort	Cáceres Hernández, Danilo
title	Real-time flood detection for video surveillance
title_short	Real-time flood detection for video surveillance
title_full	Real-time flood detection for video surveillance
title_fullStr	Real-time flood detection for video surveillance
title_full_unstemmed	Real-time flood detection for video surveillance
title_sort	real-time flood detection for video surveillance
publishDate	2018
url	https://ieeexplore.ieee.org/abstract/document/7392736/ http://ridda2.utp.ac.pa/handle/123456789/5091 http://ridda2.utp.ac.pa/handle/123456789/5091
_version_	1796209746568544256
score	12.041272

Real-time flood detection for video surveillance

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