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.