Lane Detection and Tracking Using a Parallel-snake Approach

Abstract

In this paper, we discuss the design of a parallel-snake model for lane detection and the use of a Kalman filter for tracking. The parallel-snake model is an extension of the open active contour model through the application of a parallel constraint to two open snakes. Compared with other models, this model can handle lanes with broken boundaries and reduce the convergence time with the aid of the parallel constraint and a double external energy force from two parallel snakes. To solve the problem in previous snake models, whereby the external force is lost on images with a low gradient, a balloon force is utilized to expand the double snakes from the center of the road to the lane boundaries. Because lane boundaries do not retain the parallel property, the captured images are transformed into a bird's-eye view to retrieve the parallel property of lane boundaries by planar homography. At least four corresponding points are determined and the EM-based vanishing point estimation algorithm is applied to these points to estimate the planar homography. Finally, we use a Kalman filter for parameter optimization in lane tracking considering the continuity of lane parameters between consecutive frames; i.e., to predict the parameters of subsequent frames from the previous frame and refine the estimated results to improve robustness. Experimental results show that the proposed method achieves good performance on lane datasets with shadows, variations in illumination, and broken boundaries. Furthermore, it can handle both structured and unstructured (country) roads well.