Head movement estimation for wearable eye tracker

Abstract

In the study of eye movements in natural tasks, where subjects are able to freely move in their environment, it is desirable to capture a video of the surroundings of the subject not limited to a small field of view as obtained by the scene camera of an eye tracker. Moreover, recovering the head movements could give additional information about the type of eye movement that was carried out, the overall gaze change in world coordinates, and insight into highorder perceptual strategies. Algorithms for the classification of eye movements in such natural tasks could also benefit form the additional head movement data. We propose to use an omnidirectional vision sensor consisting of a small CCD video camera and a hyperbolic mirror. The camera is mounted on an ASL eye tracker and records an image sequence at 60 Hz. Several algorithms for the extraction of rotational motion from this image sequence were implemented and compared in their performance against the measurements of a Fasttrack magnetic tracking system. Using data from the eye tracker together with the data obtained by the omnidirectional image sensor, a new algorithm for the classification of different types of eye movements based on a Hidden-Markov-Model was developed.