Head dynamic analysis: A multi-view framework

Abstract

Analysis of driver's head behavior is an integral part of driver monitoring system. In particular, head pose and dynamics are strong indicators of driver's focus of attention. In this paper, we present a distributed camera framework for head pose estimation with emphasis on the ability to operate reliably and continuously. To evaluate the proposed framework, we collected a novel head pose dataset of naturalistic on-road driving in urban streets and freeways. As oppose to utilizing all the data collected during the whole ride where for large portion of the time driver is front facing, we use data during particular maneuvers typically involving large head deviation from frontal pose. While this makes the dataset challenging, it provides an opportunity to evaluate algorithms during non-frontal glances which are of special interest to driver safety. We conduct a comparative study between proposed multi-view based approach and single-view based approach. Our analyses show promising results. © 2013 Springer-Verlag.