摘要： 针对智能车辆控制中的防碰撞问题,提出一种新的前方车辆检测与测距方法。采用多尺度分块二值模式与Adaboost提取车辆候选区域,根据候选区域内的水平边缘和灰度特征去除车辆误检,解决分类器检测过程中路面和绿化带的干扰问题。利用改进的车辆底部阴影定位方法获得车辆准确位置提高测距精度,建立基于位置信息成像模型的车距测量方法,测量前方车辆距离。实验结果表明,该方法在不同天气情况下车辆平均检测率为98.42%,车距测量平均误差为0.71 m。

关键词: 单目视觉, 车辆检测, 单目测距, 多尺度分块二值模式, 级联分类器

Abstract: Aiming at the problem of anti-collision for intelligent vehicle control,a vehicle detection and ranging method based on monocular vision is presented.Multiscale Block-Local Binary Pattern(MB-LBP) and Adaboost are used to extract vehicle candidate area,the horizontal edge and gray features are used to eliminate false detection,the problem of the interference of road and green belts is solved effectively.The improved shadow location method is used to gain exact position of the vehicle,the accuracy of distance measurement is improved.A camera model based on position information is built to measure the vehicle distance ahead.Experimental results show that the average detection rate of preceding vehicles is 98.42% and the average error of vehicle ranging is 0.71 m.

Key words: monocular vision, vehicle detection, monocular ranging, Multiscale Block-Local Binary Pattern(MB-LBP), cascade classifier

中图分类号: 

• TP391.4