FOREGROUND OBJECT SEGMENTATION FOR MOVING CAMERA SEQUENCES BASED ON FOREGROUND-BACKGROUND PROBABILISTIC MODELS AND PRIOR PROBABILITY MAPS

This paper deals with foreground object segmentation in the context of moving camera sequences. The method that we propose computes a foreground object segmentation in a MAP-MRF framework between foreground and background classes. We use region-based models to model the foreground object and the background region that surrounds the object. Moreover, the global background of the sequence is also included in the classification process by using pixel-wise color GMM. We compute the foreground segregation for each one of the frames by using a Bayesian classification and a graph-cut regularization between the classes, where the prior probability maps for both, foreground and background, are included in the formulation, thus using the cumulative knowledge of the object from the segmentation obtained in the previous frames. The results presented in the paper show how the false positive and false negative detections are reduced, meanwhile the robustness of the system is improved thanks to the use of the prior probability maps in the classification process.