Driver Fatigue Detection Based on Unscented Kalman Filter and Eye Tracking

In order to resolve the problem of fast head moving,nonlinear eye tracking and facial fatigue expression detection,a new scheme of driver fatigue detection was proposed based on unscented Kalman filter(UKF) and eye tracking.Owing to the intuition that it is easier to approximate a probability distribution than to approximate an arbitrary nonlinear function or transformation,nonlinear eye tracking can be achieved using unscented transformation(UT) by adopting a set of deterministic sigma points to match the posterior probability density function for eye movement.Driver fatigue can be detected by calculating PERCLOS(percentage of eyelid closure over the pupil over time) under a realistic driving condition after nonlinear eye tracking.The experimental results show that the proposed scheme can not only improve the robustnesses of the head rotating and fast head movement of a driver and the interference of external illuminations,but also provide more accurate estimation than the traditional Kalman filter.