Efficient Vanishing Point Detection for Driving Assistance Based on Visual Saliency Map and Image Segmentation from a Vehicle Black-Box Camera

Abstract

Techniques for detecting a vanishing point (VP) which estimates the direction of a vehicle by analyzing its relationship with surrounding objects have gained considerable attention recently. VPs can be used to support safe vehicle driving in areas such as for autonomous driving, lane-departure avoidance, distance estimation, and road-area detection, by detecting points in which parallel extension lines of objects are concentrated at a single point in a 3D space. In this paper, we proposed a method of detecting the VP in real time for applications to intelligent safe-driving support systems. In order to support safe driving of autonomous vehicles, it is necessary to drive the vehicle with the VP in center of the road image in order to prevent the vehicle from moving out of the road area while driving. Accordingly, in order to detect the VP in the road image, a method of detecting a point where straight lines intersect in an area where edge directional feature information is concentrated is required. The visual attention model and image segmentation process are applied to quickly identify candidate VPs in the area where the edge directional feature-information is concentrated and the intensity contrast difference is large. In the proposed method, VPs are detected by analyzing the edges, visual-attention regions, linear components using the Hough transform, and image segmentation results in an input image. Our experimental results have shown that the proposed method could be applied to safe-driving support systems.