Statistical Background Modeling

Note : In parenthesis, the number of papers is indicated for each method. Click on the method to see the corresponding references.

Models                                 Methods                                                                                               Authors - Dates

Gaussian Models                Single Gaussian (SG) (42)                                                                    Wren et al. (1997)

                                               Single General Gaussian (SGG) (3)                                                   Kim et al. (2007)

                                               Mixture of Gaussians (Part 1) (Part 2) (Part 3) (MOG) (344)         Stauffer and Grimson (1999)

                                               Mixture of Gaussians (MOG) [Tensor] (3 papers)                          Caseiro et al. (2010)

                                               Mixture of General Gaussians (MOGG) (6)                                      Allili et al. (2007)

                                               Kernel Density Estimation (KDE) (102)                                             Elgammal et al. (2000)

                                               Kernel Density Estimation (KDE) [Tensor] (2)                                 Caseiro et al. (2011)

Support Vector Models     Support Vector Machines (SVM)  (13)                                               Lin et al. (2002)

                                              Support Vector Regression (SVR) (3)                                                 Wang et al. (2006)

                                              Support Vector Data Description (SVDD) (6)                                   Tavakkoli et al. (2006)

Subspace Learning           1) Matrices

Models                                  PCA (PCA Gaussian or Eigenbackgrounds) (48)                            Oliver et al. (1999)

                                               PCA (PCA Riemannian Manifomds) (1)                                           Babanezhad et al. (2018)

                                               PCA (PCA Cauchy) (1)                                                                         Xie and Xing (2015)

                                               PCA (Krylov PCA) (1)                                                                           Ubaru et al. (2018)

                                              ICA (13)                                                                                                 Yamazaki et al. (2006)

                                              INMF (3)                                                                                                Bucak et al. (2007)

                                              LoPP (1)                                                                                                Gopala Krishna et al. (2012)

                                              Diffusion Bases (1)                                                                             Dushnik et al. (2013)

                                              Common Vector Approach (CVA) (2)                                               Ozkan et al. (2016)

                                              Blind Source Extarction (BSE) (1)                                                     Wang et al. (2019)

                                             2) Tensors

                                               IRT (3)                                                                                                 Li et al. (2008)

                                               TenLoPP (1)                                                                                       Gopala Krishna (2013)

                                               Common Matrix Approach (CMA) (1)                                             Isik et al. (2016)

References - Gaussians Models

C. Wren, A. Azarbayejani, T. Darrell,  A. Pentland, “Pfinder : Real-Time Tracking of the Human Body”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 19, No. 7, pages 780-785, July 1997.

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

H. Kim, R. Sakamoto, I. Kitahara, T. Toriyama, K. Kogure, “Robust Foreground Extraction Technique Using Gaussian Family Model and Multiple Thresholds”, 8th Asian Conference on Computer Vision, ACCV2007, LNCS 4843, pages 758-768, Tokyo, Japan, November 2007.

A. Elgammal, D. Harwood, L. Davis, “Non-parametric Model for Background Subtraction”, 6th European Conference on Computer Vision, ECCV 2000, pages  751-767, Dublin, Ireland, June 2000.

M. Allili, N. Bouguila, D. Ziou, “A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling”, Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, pages 503-509, 2007

References - Support Vector Models

H. Lin, T. Liu, J. Chuang, “A probabilistic SVM approach for background scene initialization”, ICIP 2002, Volume 3, pages 893-896, Rochester, New York, September 2002

J. Wang, G. Bebis, R. Miller, “Robust Video-Based Surveillance by Integrating Target Detection with Tracking”, IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum in conjunction with CVPR 2006, New York, NY, June 2006.

A. Tavakkoli, M. Nicolescu, G. Bebis, “A Novelty Detection Approach for Foreground Region Detection in Videos with Quasi-stationary Backgrounds”, ISVC 2006, pages 40-49, Lake Tahoe, USA, November 2006.

References - Subspace Learning

1) Matrices

N. Oliver, B. Rosario, A. Pentland, “A Bayesian Computer Vision System for Modeling Human Interactions”, International Conference on Vision Systems, ICVS 1999, Gran Canaria, Spain,  January 1999.

P. Xie, E. Xing, “Cauchy Principal Component Analysis”, International Conference on Learning Representations, ICLR 2015, San Diego, USA, May 2015. 

S. Ubaru, A. Seghouane, Y. Saad, "Find the dimension that counts: Fast dimension estimation and Krylov PCA", Preprint, 2018.

M. Yamazaki, G. Xu, Y. Chen, “Detection of Moving Objects by Independent Component Analysis”, ACCV 2006, pages 467-478, 2006.

S. Bucak, B. Gunsel, O. Gursoy, “Incremental Non-negative Matrix Factorization for Dynamic Background Modeling”, International Workshop on Pattern Recognition in Information Systems, Funchal, Portugal, June 2007.

M. Gopala Krishna, V. Manjunath Aradhyac, M. Ravishankar, D. Ramesh Babub, “LoPP: Locality Preserving Projections for Moving Object Detection”, International Conference on Computer, Communication, Control and Information Technology, C3IT 2012, Volume 4, pages 624-628, February 2012.

D. Dushnik, A. Schclar,  A. Averbuch, "Video segmentation via diffusion bases", Preprint, 2013.

K. Ozkan, S. Isık, O. Gerek, M. Dogan, “A new subspace based solution to background modelling and change detection”, International Journal of Intelligent Systems and Applications in Engineering, September 2016.

Q. Wang, R. Xue, Z. Sun, “Foreground estimation in video surveillance by blind source extraction”, Journal of National University of Defense Technology, Volume 41, No. 1, pages 130-141, 2019.

2) Tensors

X. Li, W. Hu, Z. Zhang, X. Zhang, “Robust Foreground Segmentation Based on Two Effective Background Models”, MIR 2008, pages 223-228, Vancouver, Canada, October 2008

M. Gopala Krishna, M. Ravishankar, D. Ramesh Babub, “Ten-LoPP: Tensor Locality Preserving Projections Approach for Moving Object Detection and Tracking”, International Conference on Computing and Information Technology, IC2IT 2013, Advances in Intelligent Systems and Computing Volume 209, pages 291-300, 2013.

S. Isık, K. Ozkan, M. Dogan, O. Gerek, “A Note on Background Subtraction by utilizing a New Tensor Approach”, International Journal of Intelligent Systems and Applications in Engineering, September 2016.