Information fusion on oversegmented images: An application for urban scene understanding

The large number of tasks one may expect from a driver assistance system leads to consider many object classes in the neighborhood of the so-called intelligent vehicle. In order to get a correct understanding of the driving scene, one has to fuse all sources of information that can be made available. In this paper, an original fusion framework working on segments of over-segmented images and based on the theory of belief functions is presented. The problem is posed as an image labeling one. It will first be applied to ground detection using three kinds of sensors. We will show how the fusion framework is flexible enough to include new sensors as well as new classes of objects, which will be shown by adding sky and vegetation classes afterward. The work was validated on real and publicly available urban driving scene data.