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from its.
Applying Multi Level Processing for Robust Geometric Lane Feature Extraction
2010 IEEE International Conference on Multisensor Fusion and
Integration for Intelligent Systems
University of Utah, Salt Lake City, UT, USA, Sept. 5-7, 2010
德国开姆尼茨工业大学
通过多级的特征提取和分类实现可靠的车道线标记测量,主要通过几何特征筛选车道线。
处理步骤为:
输入灰度图像-图像预处理-边缘特征图-多级分组-封闭的对象集-特征提取-带有特征的封闭对象集-分类-分类的对象
多级分组:先检测边缘点,在提取直线,再检测曲线,平行检测,封闭检测
方法复杂度非常高,步骤十分复杂
from its.
A Novel Lane Detection based on Geometrical Model and Gabor Filter
2010 IEEE Intelligent Vehicles Symposium
University of California, San Diego, CA, USA
June 21-24, 2010
** 导师:陈慧岩,北京理工大学车辆工程系副主任
综述了现有车道检测方法,提出一个基于几何模型和Gabor滤波的算法
使用四个参数:开始位置,车道原始转角,车道宽度,车道曲率
初始需要一次路下校准,校准使用的摄像头
第二步车道模型参数估计和模型建立,通过主方向估计和局部Hough变换得到前三个参数,对最终匹配的结果进行车道建模
第三步模型匹配,前面提到的方法匹配最佳的车道模型。
from its.
Sharp Curve Lane Boundaries Projective Model and Detection
**西北工业大学
针对对急转弯的车道曲线
一个可表达直线、急转弯车道的模型
改进的粒子群算法得到车道最大的后验概率
最后通过模型得出左右车道的曲率
from its.
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from its.