Extract the Gaze Multi-dimensional Information Analysis Driver Behavior

Recent studies has been shown that most traffic accidents are related to the driver's engagement in the driving process. Driver gaze is considered as an important cue to monitor driver distraction. While there has been marked improvement in driver gaze region estimation systems, but there are many challenges exist like cross subject test, perspectives and sensor configuration. In this paper, we propose a Convolutional Neural Networks (CNNs) based multi-model fusion gaze zone estimation systems. Our method mainly consists of two blocks, which implemented the extraction of gaze features based on RGB images and estimation of gaze based on head pose features. Based on the original input image, first general face processing model were used to detect face and localize 3D landmarks, and then extract the most relevant facial information based on it. We implement three face alignment methods to normalize the face information. For the above image-based features, using a multi-input CNN classifier can get reliable classification accuracy. In addition, we design a 2D CNN based PointNet predict the head pose representation by 3D landmarks. Finally, we evaluate our best performance model on the Eighth EmotiW Driver Gaze Prediction sub-challenge test dataset. Our model has a competitive overall accuracy of 81.5144% gaze zone estimation ability on the cross-subject test dataset.