| dc.description.abstract | To insure the safety of the driver and pedestrians on the road, we propose a vision based driver drowsiness detection and warning system. We use a camera mounted on the vehicle to capture the driver’s face images for eye detection and drowsiness discrimination.
The system consists of five parts: Image acquisition system, eye detection, eye tracking, eye open/close and gaze direction estimation, and drowsiness discrimination. In the image acquisition system, we use an IR camera with two illuminators to capture driver’s face images in poor illuminated conditions.
In order to deal with the uneven illumination, we propose a local thresholding method to divide the image into several partitions based on the strong edges then iteratively threshold each partition. We use connected-component and support vector machine (SVM) to verify eyes. If there are fixed numbers of frames succeeded in detection mode, we alternate the processing to tracking mode. In tracking mode, we detect eyes in the predicted region. We extract eye open/closed statuses and gaze directions information as our visual cues. In eye open/closed statues determination, we consider two criteria and compare their performance. In gaze direction estimation, we divide the eye region into three equal-sized subregions, then determine the pupil location in which subregion for the estimation. In drowsiness discrimination, we use PERCLOS measurement to judge whether the driver is drowsy.
We test our system on our experimental car in various illumination conditions such as sunny day, cloudy day, at night, uneven illuminated conditions, with/without glasses. From the experimental results, we find that the proposed approach can stably detect the eyes and give a warning if drowsiness is detected. | en_US |