The development of the vehicle quantity and the transportation system accompanies the rise of traffic accidents. Statistics shows that nearly 35-45% traffic accidents are due to drivers’ fatigue. If the driver’s fatigue status could be judged in advance and reminded accurately, the driving safety could be further improved. In this research, the blink frequency and eyes movement information are monitored and the statistical method was used to assess the status of the driving fatigue. The main tasks include locating the edge of the human eyes, obtaining the distance between the upper and lower eyelids for calculating the frequency of the driver's blink. The velocity and position of eyes movement are calculated by detecting the pupils’ movement. The normal eyes movement model is established and the corresponding database is updated constantly by monitoring the driver blink frequency and eyes movement during a certain period of time. The real-time data and the model calculation data are compared, and the warning is given when the eyes movement flat rate is consistent with the sleep characteristics. The study found that when the driver is in the stage of fatigue driving, the blink frequency and the saccade velocity standard deviation will be reduced. On this basis, the judgment accuracy of the driver's driving condition can be accepted. This research would play a positive role in promoting the development of real-time detection technology of driving fatigue.