摘要
在传统的基于4类特征拐点的遮挡车辆分离方法的基础上进行改进,提出了一种基于8类特征拐点的分离方法.该方法以车辆常用的矩形模板为先验知识,首先对存在遮挡的连通区域提取边缘轮廓,并将轮廓上的特征拐点分为8类;然后在对相邻且同类的轮廓特征拐点进行合并的基础上,利用改进的车辆轮廓特征拐点的类型组合来实现遮挡车辆的识别和分离.仿真实验表明,本文所提出的新方法具有更好的鲁棒性和精确性,且方法简单,具有很高的实际应用价值.
Based on the traditional method of four types feature points on contour, this paper proposes a new method of eight types feature points for the segmentation of vehicle occlusion. A rectangle region is assumed as the shape template of a vehicle. In this method, the first step is to extract the edge curve of the connected region, which has the phenomenon of vehicles occlusion, and then the feature points on contour were categorized into eight types. Subsequently, the feature points group, in which the feature points are the same kind and adjacent on contour, merged into an optimized one point according to the algorithm brought forward in the paper. At last, the vehicles were separated from each other by comparing every four adjacent feature points with the specific combination of the feature point types. Simulation results show that the new method of eight types feature points for the segmentation of vehicle occlusion is more robust, accurate, easily achieved and has good practical application merit.
出处
《北京交通大学学报》
CAS
CSCD
北大核心
2010年第5期64-68,73,共6页
JOURNAL OF BEIJING JIAOTONG UNIVERSITY
基金
河北省自然科学基金资助项目(F2010001105)
河北省教育厅科学研究项目资助(2008489)
关键词
车辆遮挡判别
轮廓特征拐点
车辆分离
拐点类型组合
vehicle occlusion detection
contour feature points
vehicle image segmentation
feature point types specific combination
作者简介
马增强（1975-），男，河北石家庄人，副教授．email：06116272@bjtu．edu．cn
杨绍普（1962-），男，河北石家庄人，教授，博士生导师．
