期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：12
页码：689-696
出版社：Science and Information Society (SAI)
摘要：Background subtraction (BGS) is one of the important
steps in many automatic video analysis applications. Several
researchers have attempted to address the challenges due to
illumination variation, shadow, camouflage, dynamic changes in
the background and bootstrapping requirement. In this paper, a
method to perform BGS using dynamic clustering is proposed.
A background model is generated using the K0
-means algorithm.
The normalized γ corrected distance values and an automatic
threshold value is used to perform the background subtraction.
The background models are updated online to handle slow illumination
changes. The experiment was conducted on CDNet2014
dataset. The experimental results show that the proposed method
is fast and performs well for baseline, camera-jitter and dynamic
background categories of video.
