基于主动表观模型及PERCLOS的疲劳检测研究

针对城市轨道交通电客车司机长时间驾驶产生的疲劳问题，目前主要是通过规章制度及警惕按钮来解决，这样既增加司机的劳动强度，也对司机疲劳驾驶检测效果有限。文章针对电客车司机现场工作环境，提出了一种主动在线实时检测方法，该方法通过视频序列分析人脸信息，采用主动表观模型完成对人眼的识别及定位，采用PERCLOS算法完成对司机疲劳检测。实验证明，该模型和算法能够很好地完成对司机的疲劳检测。

Active appearance model and PERCLOS based fatigue detection

At present, the fatigue problem of long time driving for the drivers of Urban Transit was solved by rules and alert button. These methods increase the labor intensity of the driver, the driver fatigue driving detection effect is limited. So this article put forward an active online real-time detection method based on the site work environment of drivers, the method was used to analyze the face information through a video sequence, identify and locate the human eye by using active appearance model, implement the detection of driver fatigue by PERCLOS Algorithm. Experimental results showed that the model and the Algorithm were able to implement the detection of driver fatigue.