基于眼部信息融合的疲劳驾驶检测的研究

Study on fatigue driving test based on eye information fusion

由于驾驶员疲劳驾驶产生的事故量增多,对驾驶疲劳检测的研究越发重要。通过研究目前驾驶疲劳检测的现状与发展趋势,以驾驶员个体为单位,通过AdaBoost算法对于驾驶员眼部进行特征提取,通过垂直投影法区分驾驶员是否配戴眼镜,进而对眼部进行识别。并基于特征加权贝叶斯算法将眼部疲劳参数PERCLOS值,眨眼频率以及闭眼时长这3个参数进行融合,驾驶员眼部疲劳状态进行综合判断,在MATLAB平台上进行仿真实验,对实验结果进行分析可以发现融合后的特征更能体现是否发生疲劳,一但疲劳采取语音报警提示。

As the number of accidents caused by driver fatigue driving increases,research on driving fatigue detection becomes more and more important.By studying the current situation and development trend of driving fatigue detection,this paper takes the driver's individual as the unit,extracts the features of the driver's eyes through the AdaBoost algorithm.And distinguishes whether the driver wears glasses through the vertical projection method,and then recognizes the eyes.Based on the feature-weighted Bayesian algorithm,the three parameters of eye fatigue parameters Perclos,blink frequency and closed eye duration are combined,and the driver's eye fatigue state is comprehensively judged.Simulation experiments are performed on the MATLAB platform.Analysis can be found that the characteristics of the fusion can better reflect whether fatigue occurs.Once tired,take a voice alarm prompt.