MRF-based foreground detection in image sequences from a moving camera

This paper presents a Bayesian approach for simultaneously detecting the moving objects (foregrounds) and estimating their motion in image sequences taken with a moving camera mounted on the top of a mobile robot. To model the background, the algorithm uses the GMM approach [1] for its simplicity and capability to adapt to illumination changes and small motions in the scene. To overcome the limitations of the GMM approach with its pixel-wise processing, the background model is combined with the motion cue in a maximum a posteriori probability (MAP)-MRF framework. This enables us to exploit the advantages of spatio-temporal dependencies that moving objects impose on pixels and the interdependence of motion and segmentation fields. As a result, the detected moving objects have visually attractive silhouettes and they are more accurate and less affected by noise than those obtained with simple pixel-wise methods. To enhance the segmentation accuracy, the background model is re-updated using the MAP-MRF results. Experimental results and a qualitative study of the proposed approach are presented on image sequences with a static camera as well as with a moving camera.