Real-time obstacle detection with motion features using monocular vision

Real-time obstacle detection by monocular vision is a challenging problem in autonomous navigation of vehicles and driver-assistance systems. In this paper, we introduce an approach of real-time obstacle detection which can effectively tell apart obstacles from shadows and road surface markings. We propose the followings: (1) a two consecutive frames (TCF) model to find the differences between obstacles and the ground plane by motion features; (2) a filter to increase probabilities of obstacle regions; (3) an updating process to reduce false positives and update the algorithm when the vehicle moves on. We perform experiments on two datasets and our autonomous vehicle. The results show that our method is effective in various conditions and meets the real-time requirement.