Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

NOTE: This page presents the results of some representative background modeling methods using Mixture of Gaussians on the Wallflower datasets. If you use it for publication, you have to cite the following paper

T. Bouwmans, F. El Baf, B. Vachon, “Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey”, Recent Patents on Computer Science, Volume 1, No 3, pages 219-237, November 2008.

References

[1] C. Stauffer, E. Grimson, “Adaptive background mixture models for real-time tracking”, Conference on Computer Vision and Pattern Recognition, CVPR 1999, pages 246-252, 1999.

[2] H. Wang, D. Suter ,”A Re-Evaluation of Mixture-of-Gaussian Background Modeling”, International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2005, Pennsylvania, USA, pages 1017-1020, March 2005.

[3] K. Schindler, H. Wang, “Smooth Foreground-Background Segmentation for Video Processing”, Lecture Notes in Computer Science, Asian Conference on Computer Vision, ACCV 2006, Hyderabad, India, Volume 3852, pages 581-590, January 2006.

[4] B. White, M. Shah, “Automatically Tuning Background Subtraction Parameters Using Particle Swarm Optimization”, International Conference on Multimedia and Expo, ICME 2007, Beijing, China, 2007.

[5] N. Setiawan, H. Seok-Ju, K. Jang-Woon, L. Chil-Woo “Gaussian Mixture Model in Improved HLS Color Space for Human Silhouette Extraction”, ICAT 2006, LNCS 4282, pages 732–741, 2006.