多特征融合的驾驶员疲劳程度量化方法研究

Research on the quantification method of driver fatigue level by fusing multiple features

针对疲劳驾驶检测系统鲁棒性差和疲劳程度划分过于简单的问题,利用MediaPipe人脸关键点检测技术和模糊推理系统,融合多种面部疲劳特征,研究视频序列中驾驶员疲劳程度量化评估方法,实现对驾驶员疲劳程度的实时评分和疲劳预警。首先利用MediaPipe人脸检测模型定位面部关键点;之后使用检测到的关键点从视频帧中实现对面部疲劳动态特征进行提取,得到单位时间内眨眼次数(PERCLOS)、打哈欠时长、有无瞌睡、人脸摆动近似熵四个评估指标,最后设计模糊推理系统对疲劳程度进行量化,实现对驾驶员疲劳程度的实时评估。研究表明,所提出的方法科学有效的实现了对驾驶员疲劳程度的量化评估,进一步提升了基于面部特征的疲劳驾驶检测的鲁棒性和可靠性。

Aiming at the problems of poor robustness of fatigue driving detection system and too simple classification of fatigue degree,use MediaPipe face key point detection technology and a fuzzy inference system to fuse a variety of facial fatigue features to study the quantitative assessment method of driver fatigue degree in video sequences and realize realtime scoring of driver fatigue degree and fatigue warning.In this paper,firstly,the MediaPipe face detection model is used to locate the facial key points;secondly,the detected key points are used to extract the dynamic features of facial fatigue from the video frames,and four evaluation indexes are obtained:PERCLOS,yawn length,whether or not to nod off,and the approximate entropy of face oscillation;finally,the fuzzy inference system is designed to quantify the fatigue degree and realize the real-time evaluation of driver fatigue.The proposed method shows that the proposed method is scientifically sound.The study shows that the proposed method scientifically and effectively achieves the quantitative assessment of driver fatigue degree and further improves the robustness and reliability of fatigue driving detection based on facial features.