Statistical Background Modeling

Generalized Stauffer–Grimson background subtraction for dynamic scenes (A. Chan - Department of Electrical and Computer Engineering - University of California)

A. Chan, V. Mahadevan, N. Vasconcelos, “Generalized Stauffer–Grimson background subtraction for dynamic scenes”, Machine Vision and Applications, MVA 2010, 2010.

Kernel Density Estimation

Kernel Density Estimation (Y. Sheikh, University of Central Florida, USA)

Y. Sheikh, M. Shah, "Bayesian Object Detection in Dyanamic Scenes", IEEE Conference on Computer Vision and Pattern Recognition, 2005.

Y. Sheikh, M. Shah, "Bayesian Modelling of Dynamic Scenes for Object Detection", IEEE Transactions on Pattern Analysis and Machine Vision, 2005.

Kernel Density Estimation (M. Narayana, University of Massachusetts, USA)

M. Narayana, E. Learned-Miller, A. Hanson, "Background subtraction - separating the modeling and the inference", Machine Vision and Applications, December 2013.

M. Narayana, E. Learned-Miller, A. Hanson, "Improvements in joint domain-range modeling for background subtraction", British Machine Vision Conference, BMVA 2012, 2012.

M. Narayana, E. Learned-Miller, A. Hanson, "Background modeling using adaptive pixelwise kernel variances in a hybrid feature space", IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2012, 2012.

Kernel Density Estimation (A. Elgammal - Department of Computer Science - Rutgers University)

A. Elgammal, R. Duraiswami, D. Harwood, L. Davis “Background and Foreground Modeling using Non-parametric Kernel Density Estimation for Visual Surveillance”, Proceedings of the IEEE, July 2002.

A. Elgammal, D. Harwood, L. Davis, “Non-parametric Model for Background Subtraction”, European Conference on Computer Vision, ECCV 2000, Dublin, Ireland, June 2000.

A. Elgammal, D. Harwood, L. Davis, “Non-parametric Model for Background Subtraction”, Frame Rate Workshop, International Conference on Computer Vision, ICCV 1999, Kerkyra, Greece, September 1999.

Kernel Density Estimation (T. Boult - University of Colorado - USA)

A. Elgammal, D. Harwood, L. Davis, “Non-parametric Model for Background Subtraction”, Frame Rate Workshop, International Conference on Computer Vision, ICCV 1999, Kerkyra, Greece, September 1999.

Adaptive Kernel Density Estimation (A. Tavakkoli - University of Nevada - USA)

A. Tavakkoli, M. Nicolescu, G. Bebis, M. Nicolescu, “Non-parametric Statistical Background Modeling for Efficient Foreground Region Detection”, International Journal of Machine Vision and Applications, MVA 2009, Volume 20, No 6, pages 395-409, 2009.

Subspace Learning

Multi-Subspace Learning (Y. Dong - University of Missouri - USA)

Y. Dong, G. N. DeSouza, T. X. Han, “Illumination Invariant Foreground Detection using Multi-Subspace Learning”, International Journal of Knowledge-based and Intelligent Engineering Systems, Volume 14, Number 1, pages 31-41, 2010.

Y. Dong, G. N. DeSouza, “Adaptive Learning of Multi-Subspace for Foreground Detection under Illumination Changes”, Journal of Computer Vision and Image Understanding, CVIU 2011, pages 31-49, 2011.

Support Vector Machine

Support Vector Machine (L. Cheng - Bioinformatics Institute - Singapore)

L. Cheng, M. Gong, D. Schuurmans, T. Caelli, "Real-time Discriminative Background Subtraction", IEEE Transaction on Image Processing, 2011