Driver Fatigue Detection Control System

In order to realize automatic on-line monitoring of driver fatigue state, four modules are mainly included in this system, which are image capture module, image preprocessing module, feature detection and extraction module and feature classification recognition module. Firstly, a detection system platform is built by using computer. Visual Studio and some other software and hardware equipment, and image pre-processing is carried out to detect and locate the driver face region in real-time, aiming to the shortcoming of Camshift tracking algorithm, an algorithm combining the Camshift tracking algorithm with Kalman filter is proposed to realize the real-time tracking of:human face region. And then the face model is obtained by training the sample images calibrated the facial feature points by using the gradient regression tree algorithm. The regions of eyes and mouth can be located by using this face model on the detected face. to verify the accuracy of the proposed driver fatigue detection algorithm, a fatigue driving detection experiment is carried out in the Honda's car. The driver's face images are captured by installing the COMS camera with infrared function on the front windshield, and the data are calculated and analyzed by computer. Experiment contents include the face region detection and tracking, facial features detection and state recognition, as well as fatigue recognition based on facial features and analysis. The experiment results show that the system has good accuracy, real-time and robustness, and the established driver fatigue warning can meet the real-time requirement of the driver fatigue state detection.