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

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.

[1]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[2]  Nuno Vasconcelos,et al.  Generalized Stauffer–Grimson background subtraction for dynamic scenes , 2011, Machine Vision and Applications.

[3]  Stefano Messelodi,et al.  A Kalman Filter Based Background Updating Algorithm Robust to Sharp Illumination Changes , 2005, ICIAP.

[4]  M.M. Trivedi,et al.  Vision modules for a multi-sensory bridge monitoring approach , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[5]  Brendon J. Woodford,et al.  Video background modeling: recent approaches, issues and our proposed techniques , 2013, Machine Vision and Applications.

[6]  Dar-Shyang Lee,et al.  Effective Gaussian mixture learning for video background subtraction , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Brendon J. Woodford,et al.  Localized adaptive learning of Mixture of Gaussians models for background extraction , 2010, 2010 25th International Conference of Image and Vision Computing New Zealand.

[8]  Antoine Vacavant,et al.  A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos , 2014, Comput. Vis. Image Underst..

[9]  Chunhong Pan,et al.  Effective multi-resolution background subtraction , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Mubarak Shah,et al.  Automatically Tuning Background Subtraction Parameters using Particle Swarm Optimization , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[11]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

[12]  Bertrand Vachon,et al.  Statistical Background Modeling for Foreground Detection: A Survey , 2010 .

[13]  Nigel J. B. McFarlane,et al.  Segmentation and tracking of piglets in images , 1995, Machine Vision and Applications.

[14]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  Fatih Murat Porikli,et al.  Changedetection.net: A new change detection benchmark dataset , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Serge Miguet,et al.  Real Time Foreground-Background Segmentation Using a Modified Codebook Model , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[17]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[18]  Zezhi Chen,et al.  A self-adaptive Gaussian mixture model , 2014, Comput. Vis. Image Underst..

[19]  Sridha Sridharan,et al.  Real-time adaptive background segmentation , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[20]  Thierry Bouwmans,et al.  Subspace Learning for Background Modeling: A Survey , 2009 .

[21]  Mark E Hallenbeck,et al.  Extracting Roadway Background Image , 2006 .

[22]  Thierry Bouwmans,et al.  Traditional and recent approaches in background modeling for foreground detection: An overview , 2014, Comput. Sci. Rev..

[23]  Weidong Zhang,et al.  A Novel Particle Filter Based Background Subtraction Method , 2006, 2006 International Conference on Computational Intelligence and Security.

[24]  P. Wayne Power,et al.  Understanding Background Mixture Models for Foreground Segmentation , 2002 .

[25]  Heng Zhang,et al.  Accurate Motion Detection in Dynamic Scenes Based on Ego-Motion Estimation and Optical Flow Segmentation Combined Method , 2011, 2011 Symposium on Photonics and Optoelectronics (SOPO).

[26]  Thierry Chateau,et al.  A Benchmark Dataset for Outdoor Foreground/Background Extraction , 2012, ACCV Workshops.

[27]  Arnab Roy,et al.  An Approach for Efficient Real Time Moving Object Detection , 2010, ESA.

[28]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[29]  Thierry Bouwmans,et al.  Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey , 2008 .

[30]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[31]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[32]  Thierry Bouwmans,et al.  Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey , 2011 .

[33]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..