基于机器视觉的疲劳驾驶监测预警系统

Fatigue Driving Monitoring and Early Warning System Based on Machine Vision

针对当前智能化疲劳驾驶监测产品的缺乏,本文提出一种基于机器视觉理论的设计方案。在对人眼轮廓进行拟合时,由于基于随机投票机制椭圆拟合算法的时间复杂度较高,提出一种改进的方法,通过缩小拟合点选择的范围,降低了拟合的时间复杂度;在提取人眼轮廓特征参数时,考虑到人眼大小不同和前后移动的情况,引入归一化方法,减少了特征提取的误差;在对疲劳状态进行判定时,对于PERCLOS方法不能兼顾准确度和实时性的问题,提出连续帧分析的方法,通过对参数做适当的修正,驾驶人的状态既可以得到很好的区分,同时保证了很好的实时性。实验结果表明,与改进前的算法相比,本文的算法提高了系统的准确度和实时性。

In view of the lack of current intelligent fatigue driving monitoring products,a design scheme based on machine vision theory is proposed.When fitting the eye contour,due to the high complexity of the algorithm of ellipse based on random voting mechanism,a modified method is proposed to reduce the time complexity of the algorithm,by narrowing the range of fitting points.When one judges the fatigue state,the PERCLOS method can not take into account the accuracy and real-time problem.By modifying the parameters appropriately,the method of continuous frame analysis is presented,so that the driver＇s state can be distinguished,and the real-time performance is guaranteed.Experimental results show that the algorithm improves the accuracy and real-time performance of the system compared with the previous algorithm.