Combined head localization and head pose estimation for video-based advanced driver assistance systems

Abstract

This work presents a novel approach for pedestrian head localization and head pose estimation in single images. The presented method addresses an environment of low resolution gray-value images taken from a moving camera with large variations in illumination and object appearance. The proposed algorithms are based on normalized detection confidence values of separate, pose associated classifiers. Those classifiers are trained using a modified one vs. all framework that tolerates outliers appearing in continuous head pose classes. Experiments on a large set of real world data show very good head localization and head pose estimation results even on the smallest considered head size of 7x7 pixels. These results can be obtained in a probabilistic form, which make them of a great value for pedestrian path prediction and risk assessment systems within video-based driver assistance systems or many other applications. © 2011 Springer-Verlag.