通过图像对交通事故进行识别、处理,需要将卷入交通事故的车辆等目标从交通事故场景图像中分离出来,因此建立准确的道路模型十分重要。传统的道路建模方法大都基于灰度空间,忽略了图像中的彩色信息,不适合复杂的交通环境。针对道路像素分布的非参数化特点和后续处理中对彩色信息的需求,提出一种基于色彩空间聚类的非参数化道路模型,将道路模型的建立抽象为时间轴上的色彩空间聚类过程。根据颜色变化理论,设置聚类区间为RGB颜色空间中以聚类中心和原点连线为轴的圆柱体,并通过聚类中心特征值的不同自适应来调整聚类半径。同时,对于每个像素位置,根据场景复杂程度和变化频率的不同自适应地选取聚类中心数目,获得较为准确的背景模型,在提高检测精度的同时也保证了检测效率。

It is very important to create the background model for image processing.Traditional modeling methods typically use only the gray space,ignoring information from the color space.They do not apply to the complex transportation system.In order to obtain dynamic vehicle information and colored road information for further analysis, we propose a non-parametric road modeling approach based on color spatial clustering in this article. The approach is developed according to the characteristics of pixel distribution in traffic scenes. The modeling process is abstracted as a color spatial clustering process in the time axis. According to the color distortion theory, we define a cluster interval as a cylinder whose axis is the attachment of the clustering center and the origin in color space. Meanwhile, for each pixel the clustering center number is selected adaptively according to the scene’s complexity and change frequency. In this way, the precision improves and the detection is efficient.