基于多特征融合的疲劳驾驶状态识别方法研究

Research on Fatigue Driving State Recognition Method Based on Multi-feature Fusion

针对交通安全中疲劳驾驶状态识别问题,使用单一的疲劳驾驶特征的方法识别率较低,本文提出一种基于面部多特征加权和的疲劳识别方法.通过人眼状态检测算法提取眼部疲劳参数,即持续闭眼时间、闭眼帧数比、眨眼频率,通过打哈欠状态检测得到打哈欠次数和打哈欠持续时间,通过头部运动状态分析得到点头频率,建立融合以上六个特征的驾驶疲劳状态检测模型来评估驾驶员的疲劳等级并进行相应的预警.实验测试数据选自NTHU驾驶员疲劳检测视频数据集的部分数据.经实验调整后,发现该方法的识别准确率较高,识别效果好.

Aiming at the problem of fatigue driving state recognition in traffic safety,the recognition rate of using a single fatigue driving feature is low.This paper studies and proposes a fatigue recognition method based on the weighted sum of facial multi-features.The eye fatigue parameters,such as continuous eye closing time,eye closing frame ratio and blink frequency,are extracted by human eye state detection algorithm.The number and duration of yawning are obtained through yawning state detection,the nodding frequency is obtained through head motion state analysis,and a driving fatigue state detection model integrating the above six characteristics is established to evaluate the driver’s fatigue level and give the corresponding early warning.The experimental test data are selected from part of the NTHU driver fatigue detection video data set.After experimental adjustment,it is found that this method has high recognition accuracy and provide a good recognition effect.