Extraction of lane markings using orientation and vanishing point constraints in structured road scenes

References

• C. Adam, R. Schubert, N. Mattern, and G. Wanielik, Probabilistic road estimation and lane association using radar detections, Proceedings of the 14th International Conference on Information Fusion, Chicago, IL, 2011, pp. 1–8.
   Google Scholar
• M. Aly, Real time detection of lane markers in urban streets, IEEE Intelligent Vehicles Symposium, Eindhoven, Holland, 2008, pp. 7–12.
   Google Scholar
• A. Borkar, M. Hayes, and M.T. Smith, A novel lane detection system with efficient ground truth generation, IEEE. Trans. Int. Transp. Syst. 13 (2012), pp. 365–374.
   Web of Science ®Google Scholar
• Y. Chen and J. Krumm, Probabilistic modeling of traffic lanes from GPS traces, Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, 2010, pp. 81–88.
   Google Scholar
• H.Y. Cheng, B.S. Jeng, P.T. Tseng, and K.C. Fan, Lane detection with moving vehicles in the traffic scenes, IEEE Trans. Int. Transp. Syst. 7 (2006), pp. 571–582.
   Web of Science ®Google Scholar
• K.Y. Chiu and S.F. Lin, Lane detection using color-based segmentation, IEEE Intelligent Vehicles Symposium, Las Vegas, NV, 2005, pp. 706–711.
   Google Scholar
• B. Fardi, U. Scheunert, H. Cramer, and G. Wanielik, A new approach for lane departure identification, IEEE Proceedings Intelligent Vehicles Symposium, Columbus, OH, 2003, pp. 100–105.
   Google Scholar
• M.A. Fischler and R.C. Bolles, Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM 24 (1981), pp. 381–395. doi: 10.1145/358669.358692
   Web of Science ®Google Scholar
• X.B. Gao, Z.X. Niu, D.C. Tao, and X.L. Li, Non-goal scene analysis for soccer video, Neurocomputing 74 (2011), pp. 540–548. doi: 10.1016/j.neucom.2010.09.013
   Web of Science ®Google Scholar
• R. Gopalan, T. Hong, M. Shneier, and R. Chellappa, A learning approach towards detection and tracking of lane markings, IEEE Trans. Int. Transp. Syst. 13 (2012), pp. 1–12.
   Web of Science ®Google Scholar
• Y. He, H. Wang, and B. Zhang, Color-based road detection in urban traffic scenes, IEEE Trans. Int. Transp. Syst. 5 (2004), pp. 309–318.
   Web of Science ®Google Scholar
• M.S. Javadi, M.A. Hannan, S.A. Samad, and A. Hussain, A robust vision-based lane boundaries detection approach for intelligent vehicles, Inf. Technol. J. 11 (2012), pp. 1184–1192. doi: 10.3923/itj.2012.1184.1192
   Google Scholar
• R. Jiang, R. Klette, T. Vaudrey, and S. Wang, Lane detection and tracking using a new lane model and distance transform, Machine Vis. Appl. 22 (2011), pp. 721–737.
   Web of Science ®Google Scholar
• Z. Kim, Robust lane detection and tracking in challenging scenarios, IEEE Trans. Int. Transp. Syst. 9 (2008), pp. 16–26.
   Web of Science ®Google Scholar
• C. Kreucher and S. Lakshmanan, Lana: A lane extraction algorithm that uses frequency domain features, IEEE Trans. Robot. Autom. 15 (1999), pp. 343–350.
   Google Scholar
• A. Lopez, J. Serrat, C. Canero, F. Lumbreras, and T. Graf, Robust lane markings detection and road geometry computation, Int. J. Automot. Technol. 11 (2010), pp. 395–407.
   Web of Science ®Google Scholar
• J.C. Mccall and M.M. Trivedi, Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation, IEEE Trans. Int. Transp. Syst. 7 (2006), pp. 20–37.
   Web of Science ®Google Scholar
• Z.X. Niu, Q. Tian, and X.B. Gao, Real-world trajectory extraction for attack pattern analysis in soccer video, Proceedings of the International Conference on Multimedia, Florence, Italy, 2010, pp. 635–638.
   Google Scholar
• C. Rasmussen, Grouping dominant orientations for Ill-structured road following, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, Vol. 1, 2004, pp. 470–477.
   Google Scholar
• R.K. Satzoda, S. Sathyanarayana, and T. Srikanthan, Hierarchical additive hough transform for lane detection, IEEE Embedded Syst. Lett. 2 (2010), pp. 23–26. doi: 10.1109/LES.2010.2051412
   Google Scholar
• A.A. Sewisy, Graphical techniques for detecting lines with the hough transform, Int. J. Comput. Math. 79 (2002), pp. 49–64.
   Web of Science ®Google Scholar
• R.G. Von Gioi, J. Jakubowicz, J.M. Morel, and G. Randall, LSD: A fast line segment detector with a false detection control, IEEE Trans. Pattern Anal. Mach. Intell. 32 (2010), pp. 722–732.
   PubMed Web of Science ®Google Scholar
• Y. Wang, D. Shen, and E.K. Teoh, Lane detection using spline model, Pattern Recognit. Lett. 21 (2000), pp. 677–689. doi: 10.1016/S0167-8655(00)00021-0
   Web of Science ®Google Scholar
• Y. Wang, E.K. Teoh, and D. Shen, Lane detection and tracking using b-snake, Image Vision Comput. 22 (2004), pp. 269–280. doi: 10.1016/j.imavis.2003.10.003
   Web of Science ®Google Scholar
• T. Weiss, B. Schiele, and K. Dietmayer, Robust driving path detection in urban and highway scenarios using a laser scanner and online occupancy grids, IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, 2007, pp. 184–189.
   Google Scholar
• L. Xu, E. Oja, and P. Kultanen, A new curve detection method: Randomized hough transform (rht), Pattern Recognit. Lett. 11 (1990), pp. 331–338. doi: 10.1016/0167-8655(90)90042-Z
   Web of Science ®Google Scholar