Abstract

International audience; Monocular motion analysis for advanced driver assistance systems (ADAS) is a very active research topic. However, two constraints limit the implementation of existent techniques in autonomous vehicles: poorly textured regions and large displacements due to vehicle egomotion that both lead to matching ambiguities. Coarse-to-fine strategies are generally used to deal with large motion, but the lack of large texture makes this approach inefficient to estimate road relative displacement. In this paper, we propose to assist the optical flow process by exploiting both a 3D scene model and a rough velocity estimate from either other embedded sensors or egomotion estimations from the previous frames. Using the available a priori knowledge allows to compensate the dominant flow to facilitate the estimation of the remaining part by a classical optical flow method. We give results on both synthetic and real image sequences and compare our approach to other existing methods.

Abstract

International audience; This paper deals with a new method that exploits optical flow field properties to simplify and strengthen the original c-velocity approach (Bouchafa and Zavidovique, 2012). C-velocity is a cumulative method based on a Hough-like transform adapted to velocities that allows 3D structure identification. In case of moving cameras, the 3D scene is assumed to be composed by a set of 3D planes that could be categorized in 3 main models: horizontal, lateral and vertical planes. We prove in this paper that, by using directly pixel coordinates to create what we will call the uv-velocity space, it is possible to detect 3D planes efficiently. We conduct our experiments on the KITTI optical flow dataset (Menze and Geiger, 2015) to prove our new concept besides the effectiveness of uv-velocity in detecting planes. In addition, we show how our approach could be applied to detect free navigation area (road), urban structures like buildings and obstacles from a moving camera in the context of Advanced Driver Assistance Systems.