Adaptive Cluster Based Model For Fast Video Background Subtraction

Muralikrishna, S N and Muniyal, Balachandra and Acharya, Dinesh U (2014) Adaptive Cluster Based Model For Fast Video Background Subtraction. International Journal of Advanced Computer Science and Applications,. ISSN 2158-107X

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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 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 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.

Item Type: Article
Uncontrolled Keywords: background subtraction; Gaussian mixture model; K 0 -means; clustering; object detection; transform
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 05 Apr 2021 09:23
Last Modified: 05 Apr 2021 09:23

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