# Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation
## 1 Introduction  
According to the statistics of the AAA Foundation for Traffic Safety, road accidents caused by fatigue driving comprise one-eighth of total accidents. The initial state of fatigue driving is inattention and transitory hypovigilance. With the deepening of fatigue, the driver gradually appears drowsy and eventually loses control of the vehicle. Therefore, fatigue detection of drivers would be of special interest to reduce road accidents and develop driving assistant systems. According to the existing research status, the detection methods of driver fatigue are mainly divided into three categories [1], which are vehicle-based measurements, physiological signals measurements, and visual-based measurements. Vehicle-based measurements include the assessment of steering wheel movement [2], driving deviation [3], and vehicle dynamic [4] information. The main cause of fatigue driving is human rather than vehicular. When the signals of driving performance, such as vehicle dynamics data and the steering wheel angle, change, the driver may already be in the fatigue driving stage. Using vehicle-based measurements will lead to delayed warningof fatigue driving. In addition, the detection accuracy of this method depends on personal driving habits and the driving road environment Therefore, this method is not suitable for real-time detection of driver fatigue. Physiological signal measurement methods are usually based on the driver's physiological signals, such as electroencephalogram (EEG [5]), electrooculogram (EOG [6]), and EMG [7]. The fatigue state can be evaluated by frequency domain analysis and linear classification based on EEG or EEG. However, this method needs a large number of sensors, even wearable sensors, to measure the driver's physiological signal, which may cause driver discomfort. These constraints make this method difficult to apply to the real driving environment. Compared with the other two methods, the visual method has irreplaceable advantages that are real time and non-invasive. Driver fatigue usually leads to a series of abnormal actions, such as frequent opening and closing of eyes and yawning. A visual method can help identify these actions consistently. A common visual strategy for fatigue detection mainly includes three steps: face detection, facial state recognition, and fatigue assessment. When the head deflects greatly for a long time, it is usually a precursor to low alertness and fatigue driving and the head deflection reduces the detection accuracy of the eyes and mouth state at the same time. Therefore, the head pose state should also be regarded as one of the indicators of driver fatigue. However, this problem was often ignored in the past. In recent years, this research has begun to garner wide attention [8]. This study proposes a novel driver fatigue detection framework. This framework consists of three parts: facial state recognition, head pose estimation, and fatigue assessment. Compared with the traditional visual method, this framework adds head pose estimation and takes it as an indicator of driver fatigue, which effectively improves the accuracy and robustness of this framework. The contributions of this study are as follows:  
1. In this study, a new fatigue detection system is designed that calculates whether the driver is in the fatigue driving state based on the facial state and the head posture. The performance of the system is verified according to the existing and self-built datasets. 2. In this study, the residual channel attention network (RCAN) is proposed to classify the states of eyes and the mouth, and a channel attention module is designed to be embedded in the RCAN. The attention module can adaptively extract the global semantic information of the image and significantly improve the accuracy of face state classification. Then PERCLOS and POM are used to evaluate whether the driver is in a fatigue state. 3. The EPnP method is used to estimate the camera pose and then transform it into Euler angle of the head.