Research on Driver's Gaze Zone Estimation Based on Transfer Learning

Abstract

With the increasing importance of gaze tracking technology in the detection of driver's attention and fatigue, the estimation of the driver's gaze zone has become a key point and a hot issue for safe driving. Although there has been significant improvement in the research of the driver's gaze area at present, a general gaze zone estimation system for different subjects and perspectives is still lacking. In order to better accomplish this task, this paper uses the “Columbia Gaze Data Set” that contains 56 subjects with different gaze directions and head poses to estimate the driver's gaze zone. After pre-processing the pictures such as normalization and data augmentation, the image dataset is modeled and predicted using the improved VGG16 convolutional neural network structure constructed by Keras, and the 5 head poses and 21 gaze points of each head pose in the dataset are reclassified and divided into 8 gaze areas, corresponding to the gaze zone of the driver.