Foreground Detection via Robust Low Rank Matrix Decomposition including Spatio-Temporal Constraint

Charles Guyon (1) , Thierry Bouwmans (1) , El-Hadi Zahzah (1)

1 MIA - Mathématiques, Image et Applications - EA 3165

Foreground detection is the first step in video surveillance system to detect moving objects. Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. In this paper, we propose to use a low-rank matrix factorization with IRLS scheme (Iteratively reweighted least squares) and to address in the minimization process the spatial connexity and the temporal sparseness of moving objects (e.g. outliers). Experimental results on the BMC 2012 datasets show the pertinence of the proposed approach.

Mots clés: Background Modeling; Foreground Detection; RPCA

Domaines: Traitement des images [eess.IV]

Charles Guyon, Thierry Bouwmans, El-Hadi Zahzah. Foreground Detection via Robust Low Rank Matrix Decomposition including Spatio-Temporal Constraint. International Workshop on Background Model Challenges, ACCV 2012,, Nov 2012, Daejeon, South Korea. pp.315-320. ⟨hal-00809473⟩