Homography from a vanishing point in urban scenes

In this paper, we address the problem of computing the egomotion of a vehicle in an urban environment using dynamic vision. We assume a planar piecewise world where the planes are mainly distributed along three principal directions corresponding to the axes of a reference frame linked to the ground plane with a vertical z-axis. We aim to estimate both the motion of the car and the principal planes in the scene corresponding to the road and the frontages of the building from a sequence of images provided by an on-board uncalibrated camera. In this paper, we present preliminary results concerning the robust segmentation of the road using projective properties of the scene. We develop a two-stage algorithm in order to increase robustness. The first stage detects the borders of the road using a contour-based approach and primarily allows us to estimate the Dominant Vanishing Point (DVP). The DVP and the borders of the road are then used to constrain the region where the points of interest, corresponding to the road lane markers, can be extracted. The second stage uses a robust technique based on projective invariant to match the lines and points between two consecutive images in the sequence. Finally, we compute the homography relating the points and lines lying on the road into the two images.