Portal de Periódicos da CAPES

Background Modeling via Incremental Maximum Margin Criterion

2011; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-642-22819-3_40

1611-3349

Cristina Marghes, Thierry Bouwman,

Fire Detection and Safety Systems

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.