|Table of Contents|

Lane line detection method based on orientation variance Haar feature and hyperbolic model(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2014年05期
Page:
119-126
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Lane line detection method based on orientation variance Haar feature and hyperbolic model
Author(s):
WANG Hai1;  CAI Ying-feng1;  LIN Guo-yu2;  ZHANG Wei-gong2
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
Keywords:
lane line detection;  orientation variance Haar feature;  edge distribution function;  single direction search algorithm;  hyperbolic model
PACS:
U491.223
DOI:
-
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 interest Ⅰ(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 interest Ⅱ(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 be achieved well, the detection rate in fair weather condition is 99.9%, and the detection rate in bad weather condition is 99.7%. 2 tabs, 12 figs, 16 refs.

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Last Update: 2014-10-30