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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2014  >  14(5): 119-126
WANG Hai, CAI Ying-feng, LIN Guo-yu, ZHANG Wei-gong. Lane line detection method based on orientation variance Haar feature and hyperbolic model[J]. Journal of Traffic and Transportation Engineering, 2014, 14(5): 119-126.
Citation: WANG Hai, CAI Ying-feng, LIN Guo-yu, ZHANG Wei-gong. Lane line detection method based on orientation variance Haar feature and hyperbolic model[J]. Journal of Traffic and Transportation Engineering, 2014, 14(5): 119-126.
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Lane line detection method based on orientation variance Haar feature and hyperbolic model

  • 1.

    School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China

  • 2.

    School of Instrument Science and Engineering, Southeast University, Nanjing 210096, Jiangsu, China

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  • Author Bio:

    WANG Hai(1983-), male, lecturer, PhD, +86-511-88782390, wanghail019@163.com

  • Received Date: 2014-04-29
  • Publish Date: 2014-10-25
    Abstract
  • In order to solve the problem that expressway lane lines were easily affected by many factors, which made them hard to be detected, a distributed lane line detection method based on orientation variance Haar feature and hyperbolic model was proposed.In order to get the disappearing line of lane plane in the image, the camera was calibrated firstly.Then, the lower 2/3 zone below the disappearing line was segmented as the region of interestⅠ (ROI-Ⅰ).The dip angle of straight line model of lane line in ROI-Ⅰ was obtained by using edge distribution function.Then the feature points of lane line edge were got by using orientation variance Haar feature, and the straight line model of lane line was fitted.The region of interestⅡ (ROI-Ⅱ) was determined by using the parameters of straight line model.A single direction search algorithm was proposed to get edge feature points.Full lane line model was obtained by using hyperbolic model.The lane line detection method was verified by using about 10 000 actual road images.Verification result indicates that lane line detection in a variety of conditions can beachieved well, the detection rate in fair weather condition is 99.9%, and the detection rate in bad weather condition is 99.7%.

     

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