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* Corresponding author 1 MIA - Mathématiques, Image et Applications

Abstract : Subspace learning methods are widely used in background modeling to tackle 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.

Keywords : Background subtraction Background modeling foreground detection subspace learning





Autor: Marghes Cristina - Thierry Bouwmans -

Fuente: https://hal.archives-ouvertes.fr/



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