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An Adaptive Background Subtraction Method Based on Kernel Density Estimation

In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the le...

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Detalles Bibliográficos
Autores principales: Lee, Jeisung, Park, Mignon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3478839/
http://dx.doi.org/10.3390/s120912279
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author Lee, Jeisung
Park, Mignon
author_facet Lee, Jeisung
Park, Mignon
author_sort Lee, Jeisung
collection PubMed
description In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the learning rate according to the situations. We apply an adaptive threshold method based on image changes to effectively subtract the dynamic backgrounds. The devised scheme allows the proposed method to automatically adapt to various environments and effectively extract the foreground. The method presented here exhibits good performance and is suitable for dynamic background environments. The algorithm is tested on various video sequences and compared with other state-of-the-art background subtraction methods so as to verify its performance.
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spelling pubmed-34788392012-10-30 An Adaptive Background Subtraction Method Based on Kernel Density Estimation Lee, Jeisung Park, Mignon Sensors (Basel) Article In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the learning rate according to the situations. We apply an adaptive threshold method based on image changes to effectively subtract the dynamic backgrounds. The devised scheme allows the proposed method to automatically adapt to various environments and effectively extract the foreground. The method presented here exhibits good performance and is suitable for dynamic background environments. The algorithm is tested on various video sequences and compared with other state-of-the-art background subtraction methods so as to verify its performance. Molecular Diversity Preservation International (MDPI) 2012-09-07 /pmc/articles/PMC3478839/ http://dx.doi.org/10.3390/s120912279 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Lee, Jeisung
Park, Mignon
An Adaptive Background Subtraction Method Based on Kernel Density Estimation
title An Adaptive Background Subtraction Method Based on Kernel Density Estimation
title_full An Adaptive Background Subtraction Method Based on Kernel Density Estimation
title_fullStr An Adaptive Background Subtraction Method Based on Kernel Density Estimation
title_full_unstemmed An Adaptive Background Subtraction Method Based on Kernel Density Estimation
title_short An Adaptive Background Subtraction Method Based on Kernel Density Estimation
title_sort adaptive background subtraction method based on kernel density estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3478839/
http://dx.doi.org/10.3390/s120912279
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