An Adaptive Background Subtraction Method Based on Kernel Density Estimation

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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Main Authors: Lee, Jeisung, Park, Mignon
Format: Online
Language:English
Published: Molecular Diversity Preservation International (MDPI) 2012
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3478839/
id pubmed-3478839
recordtype oai_dc
spelling pubmed-34788392012-10-30 An Adaptive Background Subtraction Method Based on Kernel Density Estimation Lee, Jeisung Park, Mignon 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/).
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Lee, Jeisung
Park, Mignon
spellingShingle Lee, Jeisung
Park, Mignon
An Adaptive Background Subtraction Method Based on Kernel Density Estimation
author_facet Lee, Jeisung
Park, Mignon
author_sort Lee, Jeisung
title An Adaptive Background Subtraction Method Based on Kernel Density Estimation
title_short 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_sort adaptive background subtraction method based on kernel density estimation
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.
publisher Molecular Diversity Preservation International (MDPI)
publishDate 2012
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3478839/
_version_ 1611918145666154496

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