摘要
结合车辆行驶的实际环境,提出了一种基于改进DBSCAN快速聚类算法的激光雷达车辆探测方法.建立激光雷达与摄像机传感器坐标与车辆坐标之间的转换模型,进行数据融合,通过改进DBSCAN算法对雷达数据进行去噪声和聚类处理,根据车辆在激光雷达探测中的形状特征模型进行形状匹配,实时完成车辆探测,并将探测结果投影至图像上.实车实验结果证明,改进的DBSCAN算法在车辆探测应用中具有良好的准确性和实时性.
Combining with the practical application of driving environment,this paper proposed a vehicle detection method based on improved DBSCAN clustering algorithm using a laser scanner. First,the vehicle and sensor coordinate conversion model was built to fuse the data from the laser scanner and the camera. Then the DBSCAN algorithm was improved to cluster the laser scanner data points and remove the noises at the same time. Based on the models of vehicle shape features,the preceding vehicles could be detected using shape matching. Finally,the result of the detection was projected onto the video image. The tests on running vehicle show that the proposed method can detect the vehicles efficiently in real traffic environment.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2010年第6期732-736,共5页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(90920304)
