Abstract
Detection of lane markings based on a camera sensor can be a low cost solution to lane departure and curve over speed warning. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue due to cast shadows, wearied and occluded markings, variable ambient lighting conditions etc. We focus on increasing the reliability of detection in two ways. Firstly, we employ a different image feature other than the commonly used edges: ridges, which we claim is better suited to this problem. Secondly, we have adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair or lane lines to the image features, based on both ridgeness and ridge orientation. In addition this fitting is performed for the left and right lane lines simultaneously, thus enforcing a consistent result. We have quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known.
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References
Jung, C.R., Kelber, C.R.: Lane following and lane departure using a linear–parabolic model. Image and Vision Computing 23, 1192–1202 (2005)
Bertozzi, M., Broggi, A., Fascioli, A.: Vision–based Intelligent Vehicles: State of the Art and Perspectives. Robotics and Autonomous Systems 32, 1–16 (2000)
López, A., Cañero, C., Serrat, J., Saludes, J., Lumbreras, F., Graf, T.: Detection of Lane Markings based on Ridgeness and RANSAC. In: IEEE Conf. on Intelligent Transportation Systems, Vienna, pp. 733–738 (2005)
López, A., Lloret, D., Serrat, J., Villanueva, J.: Multilocal Creaseness Based on the Level–Set Extrinsic Curvature. Computer Vision and Image Understanding 77, 111–144 (2000)
Dickmanns, E.D., Mysliwetz, B.D.: Recursive 3D road and relative ego–state recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 14, 199–213 (1992)
Guiducci, A.: Parametric Model of the Perspective Projection of a Road with Application to Lane Keeping and 3D Road Reconstruction. Computer Vision and Image Understanding 73, 414–427 (1999)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)
Aufrère, R., Chapuis, R., Chausse, F.: A Model–Driven Approach for Real–Time Road Recognition. Machine Vision and Applications 13, 95–107 (2001)
Fischler, M.A., Bolles, R.C.: Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM 24, 381–395 (1981)
McCall, J.C., Trivedi, M.M.: Video–based lane estimation and tracking for driver assistance: survey, system and evaluation. IEEE Trans. on Intelligent Transportation Systems 7(1), 20–37 (2006)
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López, A., Serrat, J., Cañero, C., Lumbreras, F. (2007). Robust Lane Lines Detection and Quantitative Assessment. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_36
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DOI: https://doi.org/10.1007/978-3-540-72847-4_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72846-7
Online ISBN: 978-3-540-72847-4
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