BACKGROUND MODELING THROUGH DICTIONARY LEARNING

In this work we build a model of the background based on dictionary learning. The image is divided into patches of equal size and a background model is obtained as a sparse linear combination of patch prototypes learnt from the image stream and updated when necessary to take into account stable variations. By enforcing sparsity, the obtained reconstruction can be computed and maintained effectively. The proposed method is stable with respect to illumination changes, correctly incorporates stable background changes in the model, and cancels out moving objects. Experiments on benchmark data indicate that the proposed method reaches very good pixel-wise performances even if relatively large patches are used.