Model-based Optical Flow For Large Displacements and Homogeneous Regions

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.