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 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.