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Artificial Intelligence Review

, Volume 50, Issue 2, pp 241–259 | Cite as

Review of background subtraction methods using Gaussian mixture model for video surveillance systems

  • Kalpana Goyal
  • Jyoti Singhai
Article

Abstract

Foreground detection or moving object detection is a fundamental and critical task in video surveillance systems. Background subtraction using Gaussian Mixture Model (GMM) is a widely used approach for foreground detection. Many improvements have been proposed over the original GMM developed by Stauffer and Grimson (IEEE Computer Society conference on computer vision and pattern recognition, vol 2, Los Alamitos, pp 246–252, 1999. doi: 10.1109/CVPR.1999.784637) to accommodate various challenges experienced in video surveillance systems. This paper presents a review of various background subtraction algorithms based on GMM and compares them on the basis of quantitative evaluation metrics. Their performance analysis is also presented to determine the most appropriate background subtraction algorithm for the specific application or scenario of video surveillance systems.

Keywords

Background Subtraction Background modeling Gaussian Mixture Model Foreground detection Video surveillance 

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.MANITBhopalIndia

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