| Summary: | 碩士 === 國立中央大學 === 資訊工程研究所 === 98 === The distraction of the driver is the main factor of traffic accident. Hence, the driver drowsiness detection and the driver attention detection are important in safety vehicle system. We use computer vision techniques to detect driver drowsiness or head orientation and determine the concentration of the driver. On the basis of the image coordinates and 3D coordinates of face feature points, we propose an approach of head pose estimation to infer the rotation angles of head. In order to estimate head pose accurately, we enhance feature detection to increase the precision of feature point. Feature detection contains eye and mouth detection. Considering the environment of the moving car, our method can be applied on variant-illumination and different head pose.
For eye detection, we find the suitable threshold for binarization at different illumination regions and process connected-component generation. The eye region might be connected with background because of shade or head angle. Hence, we proposed a method that separates raised region from object and apply geometric constraints to obtain eye candidates. Finally, we verify eye using support vector machines. Mouth region can be analyzed according to the location relative to eyes. Then, we detect feature points on the feature region as the 2D data for head pose estimation. We estimate head pose parameters using 3D/2D transformation and least square method according to the feature points of a personal face model.
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