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Background subtraction for night videos

Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and refl...

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Detalles Bibliográficos
Autores principales: Pan, Hongpeng, Zhu, Guofeng, Peng, Chengbin, Xiao, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205302/
https://www.ncbi.nlm.nih.gov/pubmed/34179456
http://dx.doi.org/10.7717/peerj-cs.592
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author Pan, Hongpeng
Zhu, Guofeng
Peng, Chengbin
Xiao, Qing
author_facet Pan, Hongpeng
Zhu, Guofeng
Peng, Chengbin
Xiao, Qing
author_sort Pan, Hongpeng
collection PubMed
description Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training.
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spelling pubmed-82053022021-06-24 Background subtraction for night videos Pan, Hongpeng Zhu, Guofeng Peng, Chengbin Xiao, Qing PeerJ Comput Sci Algorithms and Analysis of Algorithms Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training. PeerJ Inc. 2021-06-10 /pmc/articles/PMC8205302/ /pubmed/34179456 http://dx.doi.org/10.7717/peerj-cs.592 Text en ©2021 Pan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Pan, Hongpeng
Zhu, Guofeng
Peng, Chengbin
Xiao, Qing
Background subtraction for night videos
title Background subtraction for night videos
title_full Background subtraction for night videos
title_fullStr Background subtraction for night videos
title_full_unstemmed Background subtraction for night videos
title_short Background subtraction for night videos
title_sort background subtraction for night videos
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205302/
https://www.ncbi.nlm.nih.gov/pubmed/34179456
http://dx.doi.org/10.7717/peerj-cs.592
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