Background Subtraction Research

My research in background subtraction concerns 1) full surveys in the field for novice and experts, 2) investigation of three mathematical concepts to model the background, and classify pixels as background or foreground, and 3) robust features and feature selection for background/foreground separation. Thus, my main research can be summarized in the following points:

  • A Survey Approach: The surveys concern sub-categories of models (Mixture of Gaussians, Subspace Learning, RPCA), categories of models (Fuzzy, Decomposition in Low-rank+ Additive Matrices, Statistical), and all the categories of models. (6 Chapters, 6 Journals)

  • A Classical Approach:  This research concerns the improvement of the most investigated method in statistical background modeling, that is the Mixture of Gaussians (MOG). (1 Conference)

  • A Fuzzy Approach: This research introduces fuzzy concepts in the different steps of background subtraction. Type-2 Fuzzy Gaussian is used for background modeling, Choquet Integral is used in foreground detection, An adaptive fuzzy scheme is used for background maintenance via a fuzzy  adaptive learning rate. (1 Chapter, 1 Journal, 6 Conferences)

  • A Subspace Learning Approach: This research concerns the use of discriminative and mixed subspace learning in background modeling and foreground detection. Thus, IMMC is used as a discriminative approach and PCA-LDA is used as a mixed approach. (2 Journals, 3 Conferences)

  • A Decomposition into Low rank plus Additive Matrices Approach: This research firstly concerns the evaluation of the RPCA matrix model for background/foreground separation and background initialization. Secondly, it investigates the application of similar problem formulations (RPCA, RNMF, RMC, RST, RLRM) based on decomposition into low rank plus additive matrices. (3 Chapters, 6 Journals, 17 Conferences)

  • A Decomposition into Low rank plus Additive Tensors Approach: This research concerns the evaluation of the RPCA tensor model for background/foreground separation and background initialization. Then, we introduce online decomposition into low rank plus additive tensors for background/foreground separation and background initialization. (4 Conferences)

  • Robust Features and Feature Selection: This research concern the developement of robust texture features and feature selection for robust background/foreground separation. (2 Journals, 3 Conferences)

Publications (53) : 7 (10-3) chapters, 15 Journals, 31 conferences.

Note: My publications are available on Academia, Research Gate, Researchr, ORCID and Publication List.