Subspace learning methods are widely used in background modeling to be robust to illumination changes.Their main advantage is that it doesn’t need to label data during the training and running phase. Recently, White et al. [1] have shown that a supervised approach can improved significantly the robustness in background modeling. Following this idea, we propose to model the background via a supervised subspace learning called Incremental Maximum Margin Criterion (IMMC). The proposed scheme enables to initialize robustly the background and to update incrementally the eigenvectors and eigenvalues. Experimental results made on the Wallflower datasets show the pertinence of the proposed approach. (more information)
D. Farcas, C. Marghes, T. Bouwmans, “Background Subtraction via Incremental Maximum Margin Criterion: A discriminative approach”, Machine Vision and Applications, March 2012.
C. Marghes, T. Bouwmans, "Background Modeling via Incremental Maximum Margin Criterion", International Workshop on Subspace Methods, ACCV 2010 Workshop Subspace 2010, Queenstown, New Zealand, November 2010.
D. Farcas, T. Bouwmans, "Background Modeling via a Supervised Subspace Learning", International Conference on Image, Video Processing and Computer Vision, IVPCV 2010, pages 1-7, Orlando, USA , July 2010.