Advanced Statistical Background Modeling

Categories                                Methods                                                                                                             Authors - Dates

Mixture Models                     Assymetric Generalized Gaussian Mixture Model (AGMM) (4)                  Elguebaly and Bouguila (2013)

                                                  Student-t's Mixture Model (STMM) (2)                                                          Mukherjee and Wu (2012)

                          Dirichlet Processs Gaussian Mixture Model (DP-GMM) (3)                       He et al. (2011)

                                                  Variational Dirichlet Mixture Model (varDMM) (2)                                      Fan and Bouguila (2012)

                                                  Inverted Dirichlet  based Distributions (IDMM) (1)                                     Fan and Bouguila (2019)

                                                  Poisson Mixture Model (PMM) (2)                                                                  Faro et al. (2011)

                                                  Bivariate Poisson Model (BPP) (2)                                                                   Zhin et al. (2014)

                                                  Cauchy Mixture Model (CMM) (1)                                                                   Sowmiya et al. (2019)

                                                  Gamma Mixture Model (1)                                                                              Zin et al. (2020)

Hybrid Models                        KDE-GMM (KGMM) (1)                                                                                      Ding et al. (2011)

                                                  KDE-GMM Hybrid Model (KHGM) (2)                                                             Liu et al. (2008)

Advanced Models                  Video Background Extractor (ViBe) (71)                                                        Barnich et al. (2009)

                                                  Pixel-Based Adaptive Segmenter (PBAS) (9)                                                 Hofman et al. (2012)

                                                  Block-based Adaptive Segmenter (BBAS) (1)                                                Muchtar et al. (2018)

Mixture Models

T. Elguebaly, N. Bouguila, “Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection”, Machine Vision and Applications, 2013.

D. Mukherjee, J.Wu, “Real-time Video Segmentation using Student-t's Mixture Model”, International Conference on Ambient Systems, Networks and Technologies, ANT 2012; pages 153-160, 2012.

Y. He, D. Wang, M. Zhu, “Background subtraction based on nonparametric Bayesian estimation”, International Conference Digital Image Processing, July 2011.

W. Fan, N. Bouguila, “Online variational learning of finite Dirichlet mixture models”, Evolving Systems, January 2012.

W. Fan, N. Bouguila, "Nonparametric Hierarchical Bayesian Models for Positive Data Clustering based on Inverted Dirichlet-based Distributions", IEEE Access, 2019.

A. Faro, D. Giordano, C. Spampinato, "Adaptive Background Modeling Integrated with Luminosity Sensors and Occlusion Processing for Reliable Vehicle Detection", IEEE Transactions on Intelligent Transportation Systems, Volume 12, No. 4, pages 1398-1412, December 2011.

T. Zin, P. Tin, T. Toriu, H. Hama, "A New Background Subtraction Method Using Bivariate Poisson Process", International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pages 419-422, August 2014.

D. Sowmiya, P. Anandhakumar, "Cauchy Mixture Model-based Foreground Object Detection with New Dynamic Learning Rate Using Spatial and Statistical information for Video Surveillance Applications", National Academy of Sciences, India Section A: Physical Sciences, pages 1-14, June 2019.

T. Zin, P. Tin, C. Phyo, "Motion Detection Method for Reducing Foreground Aperture Problem in Background Modelling", IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020, pages 260-261, Kyoto, Japan, 2020.

Hybrid Models

J. Ding, M. Li, K. Huang, T. Tan, “Modeling Complex Scenes for Accurate Moving Objects Segmentation”, Asian Conference on Computer Vision, ACCV 2010, pages 82-94, 2010.

Z. Liu, W. Chen, K. Huang, T. Tan, “A Probabilistic Framework Based on KDE-GMM Hybrid Model for Moving Object Segmentation in Dynamic Scenes”, International Workshop on Visual Surveillance, ECCV 2008, October 2008.

Advanced Models

O. Barnich, M. Van Droogenbroeck, “ViBe: a powerful random technique to estimate the background in video sequences”, International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, pages 945-948, April 2009.

M. Hofmann, P.Tiefenbacher, G. Rigoll, "Background Segmentation with Feedback: The Pixel-Based Adaptive Segmenter",  IEEE Workshop on Change Detection, CVPR 2012, June 2012

K. Muchtar, F. Rahman, T. Cenggoro, A. Budiarto, B. Pardamean, "An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter", Procedia Computer Science, Volume 135, pages 579-586, 2018.