文章基本信息

标题：Adaptive Cluster based Model for Fast Video Background Subtraction
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作者：Muralikrishna SN ; Balachandra Muniyal ; U Dinesh Acharya 等
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
DOI：10.14569/IJACSA.2019.0101288
出版社：Science and Information Society (SAI)
摘要：Background subtraction (BGS) is one of the impor-tant 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 K -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 illu-mination 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.
关键词：Background subtraction; Gaussian mixture model; -means; clustering; object detection; transform