Driver distraction analysis using face pose cues

Abstract

Vehicle driver distraction is one of the major reasons for road accidents. Involvement with a co-passenger, use of in-vehicle devices or phone leads to a situation where the driver head pose varies and the eye is off the road. A low cost early warning system should reduce the distracted driving instances, thus making our roads safer. Face pose information forms an important cue to determine driver distraction. The main objective of this work is to analyse the distractions of the driver based on his/her face pose cues. A straight pose or slight variation would indicate a non-distracted driver, while a large pose variation from the center would indicate a high probability for a distracted driver. Face pose database of vehicle drivers is developed and is bench-marked. A clustered two layer approach on Gabor features is proposed. A five layer convolutional network with three fully connected layers is also used to bench-mark the data. The proposed clustered two-layer approach with Gabor features and SVM classifier provides better results in driver distraction analysis when compared to the deep learning approach and other manifold approaches. The improved accuracy could be attributed to the improved modeling of manifold in our approach, better class discrimination of the Gabor features together with better classification provided by the SVM classifier.