An Adaptive Eye Gaze Tracking System Without Calibration for Use in an Automobile

Abstract

One of the biggest hurdles to the development of an effective driver state monitor is the that there is no real-time eye-gaze detection. This is primarily due to the fact that such systems require calibration. In this thesis the various aspects that comprise an eye gaze tracker are investigated. From that we developed an eye gaze tracker for automobiles that does not require calibration. We used a monocular camera system with IR light sources placed in each of the three mirrors. The camera system created the bright-pupil effect for robust pupil detection and tracking. We developed an SVM based algorithm for initial eye candidate detection; after that the eyes were tracked using a hybrid Kalman/Mean-shift algorithm. From the tracked pupils, various features such as the location of the glints (reflections in the pupil from the IR light sources) were extracted. This information is then fed into a Generalized Regression Neural Network (GRNN). The GRNN then maps this information into one of thirteen gaze regions in the vehicle.