Study On Monitoring Driver Distraction Based On Face Orientation

Safety problem is a perpetual theme in the development of automobile traffic. With the rapid increase of vehicle conservation and the enhancement of highway level, traffic accidents occurred more and more frequently. Under this kind of status, the intelligent transportation occurred. Safety Driving Assist (SDA), mainly solving the problem of road safety, is a key component of the Intelligent Transportation System. Simultaneously, SDA also can relax the pressure of traffic jam and environmental pollution. At present, Europe, America and other developed countries have invested massive resources in this field and obtained many valuable researches.On the vehicle active safety consideration, this thesis carries on some positive beneficial study in the field of driver's status monitoring based on machine vision. The purpose of the study is reducing the traffic accidents, through monitoring the driver's face features real-timely and giving warning message in time, and providing theory and technology support for the SDA research of our country.According to the domestic and international research status in this field, the thesis mainly has carried on some studies about the following several topics. The face segmentation is the basis of locating face and other features such as eyes and mouth. In this thesis, the brightness correction and white balance have been conducted in the image pre-processing. As the environment illumination and the image collector system's influence, the image used for face location possibly appears brightness and color disproportion. Therefore, the brightness Gamam correction method is used for solving the brightness influence and the white balance based on Grey World Model is introduced for solving the image color disproportion. Test results show that the method adopted can adjust the image quality very well.The face and features location is the precondition of face orientation estimating. At present, there are many methods for face location. These methods can be divided into two types roughly: method based on knowledge and on statistical characteristics. They have their own advantages and shortcomings: the former can detect face rapidly, but its precision is lower than the latter's. Therefore, this paper uses AdaBoost classifier based on knowledge to detect the possible face ROI in image. In the ROI, the skin color model based on statistical characteristics is adopted to locate face region accurately. For the eye and mouth detection, methods based on grey projection and Fisher linear transformation are used to locate the regions accurately.The face's contour detection is the key component of face orientation judgment in this thesis. On the basis of the fact that face's contour looks like an ellipse, a method based on edge point restriction is proposed to fit the outline's curve. In the process of ellipse fitting, three restraint factors (face geometry restraint, the curvature symmetrical phase restraint and the edge point coordinates restraint) are used. All of the above process steps provide an insurance of the ellipse fitting precision and the face orientation estimating result.Using monocular vision to analysis the face orientation is an estimating method of acquiring face 3D information. Research result shows that the eye and mouth's position in face region could be changed when the driver's face occurs deflecting. Based on this fact, with the help of face edge point, eyes and mouth region detection, BP neural net is used to estimate the face's orientation.The effect of face and features tracking is the key component of the system developed. MeanShift tracking method based on target color characteristics has the merits of quick speed and strong robustness. But it is sensitive to the target moving speed and the object looks like the target in the background. Considering the Kalman filter's advantages such as simple computation and quick speed, this thesis combines the Kalman filter with MeanShift algorithm to carry on the tracking task. Firstly, Kalman filter is used to forecast the possible target's region in the image. Then the MeanShift algorithm is adopted to locate the target accurately in the possible area. On the one hand this method enhances the tracking speed; simultaneously it also provides an insurance of tracking accuracy.In summary, many systematic and scientific researches have carried on in this thesis, which are the key technologies in Vision-based Driver's face orientation monitoring. The achievements not only can be adopted by the product research, but also can provide technical and theoretical support for deep research in SDA field.