Large head movement tracking using sift-based registration

Abstract

Although there exists dozens of vision based 3D head tracking methods, none of them considers the problem of large motion, especially the movement along the Z axis. In this paper we propose a novel tracking method to handle this problem by using Scale Invariant Feature Transform (SIFT) based registration algorithm. Salient SIFT features are first detected and tracked between two images, and then the 3D points corresponding to these features are obtained from a stereo camera. With these 3D points, a registration algorithm in a RANSAC framework is employed to detect the outliers and estimate the head pose. Performance evaluation shows an accurate pose recovery (3° RMS) when the head has large motion, even with movement along the Z axis was about 150 cm.