摘要
疲劳驾驶是导致交通事故的主要原因之一,因此,以人眼检测为核心的疲劳驾驶检测受到了普遍地关注。目前传统的疲劳驾驶检测方法采用"人脸-人眼"模型,即先定位人脸,随后再进行人眼检测。在Ada Boost算法的基础上,使用变模板匹配方法进行人眼检测,并改进了"人脸-人眼模型",采用多特征的分类器来有选择的跳过人脸检测步骤,从而快速准确地定位人眼。在DM642芯片上进行实验,人眼识别率达到了90%以上,且平均每秒能处理40帧图片,证明了该方法的高效性和实用性。
Since fatigue driving is one of the main causes of traffic accidents,the fatigue drivingdetection based on eye de-tection has been concerned generally. At present,＂human face-eye＂model is mainly used in the traditional fatigue driving de-tection,in which Ada Boost algorithm is used to locate human face,and then the human eye detection is performed. In this pa-per,a template-changed matching method is used to make human eye detection based on the Ada Boost algorithm,and im-prove the＂human face-eye＂model. A multi-feature classifier is adopted to skip the face detection step selectively,so as to lo-cate the human eyes more quickly and accurately. An experiment was carried out on DM642 chip. The result shows that the hu-man eye recognition rate is more than 90%,and the average processing speed is 40 images per second,which has proved theefficiency and practicality of the method.
出处
《现代电子技术》
北大核心
2015年第4期87-90,共4页
Modern Electronics Technique
基金
国家自然科学基金重点项目(61233011)
关键词
人眼检测
疲劳驾驶
变模板匹配
LBP
多特征分类器
human eye detection
fatigue driving
template-changed matching
LBP
multi-feature classifier
