Driver fatigue detection method based on multi-feature empirical fusion
To address the problem of traffic accidents caused by driver fatigue,this paper proposes a visual driver fatigue detection method based on multi-feature experience fusion.First,the driver's facial state features,including eye features and mouth features,are captured and extracted in real-time,and an experience fusion model is used to analyze these features.Then,the multi-dimensional facial behavior information is mapped to the Karolinska sleepiness scale(KSS)score to assess the driver's fatigue level.Finally,an experiment is conducted to verify the accuracy,reliability,and effectiveness of the detection method.The experimental results show that the accuracy of the detection method for different fatigue levels is 90.34%for the alert state,90.17%for mild fatigue,90.46%for moderate fatigue,and 97.67%for severe fatigue.This detection method can accurately assess the driver's fatigue level and provide effective technical support for improving driving safety.
万方数据
维普
