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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Antonio Lopez; Joan Serrat; J. Saludes; Cristina Cañero; Felipe Lumbreras; T. Graf |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Ridgeness for Detecting Lane Markings |
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2005 |
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2nd International Workshop on Intelligent Transportation Systems (WIT2005), Conference Proceedings (Sponsored by the IEEE Communication Society, Germany Chapter) |
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lane markings |
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Hamburg (Germany) |
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ADAS @ adas @ LSS2005 |
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548 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras; T. Graf |
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Title |
Robust lane markings detection and road geometry computation |
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Journal Article |
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2010 |
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International Journal of Automotive Technology |
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IJAT |
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11 |
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3 |
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395–407 |
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lane markings |
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Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known. |
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The Korean Society of Automotive Engineers |
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1229-9138 |
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ADAS @ adas @ LSC2010 |
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1300 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Robust Lane Lines Detection and Quantitative Assessment |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis |
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4477 |
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274–281 |
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lane markings |
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Girona (Spain) |
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J. Marti et al |
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IbPRIA |
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ADAS @ adas @ LSC2007 |
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881 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Antonio Lopez; Cristina Cañero; Joan Serrat; J. Saludes; Felipe Lumbreras; T. Graf |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Detection of lane markings based on ridgeness and RANSAC |
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Miscellaneous |
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2005 |
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Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 733–738 |
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lane markings |
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Vienna (Austria) |
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ADAS @ adas @ LCS2005 |
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