A driver fatigue detection method based on improved Kalman filter algorithm under infrared conditions

Fatigue driving is one of the main causes of traffic accidents. In this paper, face region detection and driver state analysis are investigated using infrared video images. An improved Kalman filter algorithm is proposed to track the human face, and geometric constraints are applied to the driver's eye detection region and mouth detection region using the three court and five eye features. The camshift algorithm is used for face feature recognition. Based on the conclusion of statistical analysis, the driver status is judged by combining the detection results of human eye status and mouth status. The algorithm is experimentally verified using different driver video images taken under different lighting conditions, and the results show that the accuracy of the algorithm proposed in this paper reaches more than 96%, and the detection of the driver state is in line with the real cab environment, which is of practical application value.