Lane Detection Using Labeling Based RANSAC Algorithm
In this paper, we propose labeling based RANSAC algorithm for lane detection. Advanced driver assistance systems (ADAS) have been widely researched to avoid unexpected accidents. Lane detection is a necessary system to assist keeping lane and lane departure prevention. The proposed vision based lane detection method applies Canny edge detection, inverse perspective mapping (IPM), K-means algorithm, mathematical morphology operations and 8 connected-component labeling. Next, random samples are selected from each labeling region for RANSAC. The sampling method selects the points of lane with a high probability. Finally, lane parameters of straight line or curve equations are estimated. Through the simulations tested on video recorded at daytime and nighttime, we show that the proposed method has better performance than the existing RANSAC algorithm in various environments.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316472Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 967
 T. Hummel, R. Chauhan, R. Srivastava, M. Baruah, “Adaptive driver assistance systems,” German Insurance Association Insurers Accident Research. Available on www.udv.de, 2015.
 A. L. Kesidis, and N. Papamarkos, “On the gray-scale inverse hough transform," Image and Vision Computing, vol. 18, no. 8, pp. 607-618, 2000.
 A. Borkar, M. Hayes, and M. T. Smith. “Robust lane detection and tracking with RANSAC and kalman filter," IEEE International Conference on Image Processing. pp. 3261-3264, 2009.
 Y. Wang, E. K. Teoh, and D. Shen. “Lane detection and tracking using B-Snake," Image and Vision computing, vol. 22, no. 4, pp. 269-280, 2003.
 Y. Wang, D. Shen, and E. K. Teoh, “Lane detection using spline model," Pattern Recognition Letters, vol. 21, no. 8, pp. 677-689, 2000.
 M. Nieto, L. Salgado, and F. Jaureguizar, “Stabilization of inverse perspective mapping images based on robust vanishing point estimation," Proceedings of IEEE Intelligent Vehicles Symposium, pp. 315-320, 2007.
 J. Canny, “A computational approach to edge detection," Readings in Computer Vision, pp. 184-203, 1987.
 R. C. Gonzalez, and R. E. Woods, Digital image processing 3rd. Pearson Hall.
 H. H. Bock, “Clustering methods: a history of k-means algorithms," Springer Selected contributions in data analysis and classification, pp. 161-172, 2007.
 M. Fischler, and R. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, no. 6, pp. 381-395, 1981.
 W. Gander, G. H. Golub, and R. Strebel, “Least-squares fitting of circles and ellipses," BIT Numerical Mathematics vol. 34, no.4 pp. 558-578, 1994.