Lane detection using spline model for freeway aerial videos

Abstract

This paper proposes a lane detection algorithm based on spline model in aerial video of the freeway, which consists of three sections: image segmentation, clustering of lane feature points and lane model parameter estimation. Firstly, the segmentation method is based on the characteristics of lane, such as color, width and shape. In the aspect of clustering of lane feature points, spectral clustering algorithm is used to accomplish the clustering of effective feature points, and the similarity matrix is constructed according to the line spacing. In terms of lane model selection and parameter estimation, we fulfill them with the following three procedures: 1) cubic B-spline curve is used in this paper to express the lane more accurately and to indicate the distance farther. 2) we evaluated the model parameters by taking advantage of the improved RANSAC algorithm. 3) we chose the Kalman filter to correct and predict lane parameters. The results of experiment demonstrate that the proposed method can detect the model parameters of every lane from the video of aerial photography freeway with high stability and high detection accuracy.