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DOI: 10.14569/IJACSA.2019.0101288
PDF

Adaptive Cluster based Model for Fast Video Background Subtraction

Author 1: Muralikrishna SN
Author 2: Balachandra Muniyal
Author 3: U Dinesh Acharya

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 12, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: 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.

Keywords: Background subtraction; Gaussian mixture model; -means; clustering; object detection; transform

Muralikrishna SN, Balachandra Muniyal and U Dinesh Acharya, “Adaptive Cluster based Model for Fast Video Background Subtraction” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101288

@article{SN2019,
title = {Adaptive Cluster based Model for Fast Video Background Subtraction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101288},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101288},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {Muralikrishna SN and Balachandra Muniyal and U Dinesh Acharya}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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