Study On Features Recognition And Detection Of Driver Visual Distraction Based On Video Analysis

Many studies reveal that visual distraction degrades driving performance and it is the main reason of traffic accidents. The distraction problem may be expanded in the near future, as many drivers will use an increasing number of electronic devices such as cell phones, navigation systems, and wireless Internet. A detection system that can predict distraction and alert the driver by monitoring driver faces' features could reduce the number of distraction crashes.In this thesis we start with analyzing the effects of visual distraction on driving performance. Then the study of visual distraction detection is developed based on video analysis. And we focus on developing methods to extract features of visual distraction based on video analyzing. In this paper the main work is as follows:First, effects of visual distraction on driving performance have been analyzed. An experiment is designed to test the effects of visual distractions in different positions have on driver. The SDLPs (standard deviation of lane positions) are analyzed when driver read two kinds of texts in four different positions. We conclude that the effects degree is deeper as the visual distraction departure father. Methods of detecting driver visual distraction are studied. A driver visual distraction detection model is constructed based on visual line. And turn activity recognition is embraced in this model.Then face detection and face features location has been studied under variety face pose. We propose to predict face region using mixture-of-Gaussians modeling of face color first. And precise face region is located base on eyebrow and lips locating. We propose to locate eyebrow using combine projection function because intensity of eyebrow is low and change acutely. A method to get rid of background is proposed to enhance precision of eyebrow locating when face turn to one side. The lips' region is located based on color quadratic polynomial model. The information that lips color is redder than face's is used to locate lips' region further. The normalize method which is fit for resizing face image has also been studied in this paper.Kernel Principal Component Analysis (KPCA) is proposed to estimate driver face pose. Getting the standard face images with exact pose is the first step, so we design an images collection system. The manifold in high dimension face images can be embodied into low dimension space. So a standard curve to estimate head pose is constructed. A circle is fit using face pose curve. A new sample's pose can be calculated using the centre of the circle and the two points which are nearest to the new point in pose curve. The kernel functions and their parameters' effects on pose angle estimation precision are also studied.A method based on Multi-PCA (Principal Component Analysis) to recognize eye gaze direction has been proposed in this paper. The principle of PCA is studied and its shortage is analyzed too. First, the eye gaze direction is classified into five kinds and feature space is constructed for every kind. Then, the common statistical features of each space are extracted. Finally, the test samples are classified based on their reconstructing errors in different feature spaces. The experiments show that Multi-PCA gets a higher recognition rate than PCA because the method takes full advantage of sole features.Driver gaze will departure the front of vehicle for more than two seconds when driver turns at the intersections of the ways. So the driver visual distraction detection system will take this as visual distraction wrongly. A method is proposed to recognize driver turn activities to reduce percents of negative alarm. The standard deviation of driver hands' positions is used. Particle filter based tracking method is proposed to enhance the speed of locating driver hands.Last a visual distraction detection system comprising hardware and software has been constructed and detecting experiments are carried out. Videos of driver monitoring the panel, adjusting radio, turning in the intersection are getten when driver is on the road. The features of visual distraction are extracted using methods proposed in this thesis. The experiments are carried out to test visual distraction detecting method. The experiments result that the method is effective. And the method can avoid recognizing turn activities as visual distraction.