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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2012
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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. |
format | Online Article Text |
id | pubmed-3478839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
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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