摘要
基于视觉的机动车辆自动识别方法工程量小、检测范围大、系统安装相对灵活，是一种很有前途的方法。目前已开发的商业系统大都用在日间辅助交通管理上。为改善视觉交通检测系统的实用性和增强系统的功能，本文作者对模型系统进行了深入的理论研究，提出了２．５维图像的新思路，提取车辆外形的综和特征更为准确，自适应识别方案使车型识别准确灵活，车头颜色特征的利用使识别的准确性进一步提高。实验证明，系统对光线的适应能力也明显增强，系统的稳定性得到改善，有望扩大系统的使用范围。
Video based vehicle detecting system is a profitable way in an intelligent automobile/highway controlling system and traffic monitoring system.It is possible to have a large detection area and a flexible installation way.To improve the usability of the video based real time intelligent traffic monitoring system,a new idea by using 2.5 dimensions time space image has been introduced.First,color changing and intensity transforming should modify images.Second,more features of objects including length,width,and fuzzy height information can be extracted by object's shape analysis。Thirdly,a supervised learning method using a neuro network realizes a self adaptive recognition system.Then combined with the color information it can give a more accurate result.All the improvement endows the system with a desirable performance.
出处
《国防科技大学学报》
EI
CAS
CSCD
1998年第5期33-38,共6页
Journal of National University of Defense Technology
关键词
机动车辆
自动识别系统
交通检测系统
automobile,video based image,2.5 dimensions time space image,color feature and feature selection,automobile's feature selection and extraction,automatic vehicle recognition
