Enhanced Bayesian foreground segmentation using Brightness and Color Distortion region-based model for shadow removal

In this paper we present a novel foreground segmentation system for monocular static camera sequences and indoor scenarios that achieves a correct shadow removal via global MAP-MRF framework formulation for the foreground, background and shadow classification task. We propose to combine a region-based spatial-color foreground model and a pixel-wise background model in the RGB domain with an spatial-Brightness Distortion (BD) and Color Distortion (CD) shadow model which present specific features to classify potential shadow regions. The results presented in the paper show the improvement of the system avoiding the necessity of thresholds for shadow detection task and reducing false positive and false negative detections originated by the shadow effects that other methods of the state of the art present.