Sensors 2012, 12(9), 12279-12300; doi:10.3390/s120912279
Article

An Adaptive Background Subtraction Method Based on Kernel Density Estimation

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Received: 23 July 2012; in revised form: 4 September 2012 / Accepted: 4 September 2012 / Published: 7 September 2012
(This article belongs to the Section Physical Sensors)
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Abstract: 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.
Keywords: background subtraction; kernel density estimation; video surveillance; adaptive background estimation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Lee, J.; Park, M. An Adaptive Background Subtraction Method Based on Kernel Density Estimation. Sensors 2012, 12, 12279-12300.

AMA Style

Lee J, Park M. An Adaptive Background Subtraction Method Based on Kernel Density Estimation. Sensors. 2012; 12(9):12279-12300.

Chicago/Turabian Style

Lee, Jeisung; Park, Mignon. 2012. "An Adaptive Background Subtraction Method Based on Kernel Density Estimation." Sensors 12, no. 9: 12279-12300.


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