Rights statement: © ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems http://doi.acm.org/10.1145/3290605.3300842
Accepted author manuscript, 1.1 MB, PDF document
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Resolving Target Ambiguity in 3D Gaze Interaction through VOR Depth Estimation
AU - Mardanbegi, Diako
AU - Langlotz, Tobias
AU - Gellersen, Hans-Werner Georg
N1 - © ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems http://doi.acm.org/10.1145/3290605.3300842
PY - 2019/5/4
Y1 - 2019/5/4
N2 - Target disambiguation is a common problem in gaze interfaces, as eye tracking has accuracy and precision limitations. In 3D environments this is compounded by objects overlapping in the field of view, as a result of their positioning at different depth with partial occlusion. We introduce \textit{VOR depth estimation}, a method based on the vestibulo-ocular reflex of the eyes in compensation of head movement, and explore its application to resolve target ambiguity. The method estimates gaze depth by comparing the rotations of the eye and the head when the users look at a target and deliberately rotate their head. We show that VOR eye movement presents an alternative to vergence for gaze depth estimation, that is feasible also with monocular tracking. In an evaluation of its use for target disambiguation, our method outperforms vergence for targets presented at greater depth.
AB - Target disambiguation is a common problem in gaze interfaces, as eye tracking has accuracy and precision limitations. In 3D environments this is compounded by objects overlapping in the field of view, as a result of their positioning at different depth with partial occlusion. We introduce \textit{VOR depth estimation}, a method based on the vestibulo-ocular reflex of the eyes in compensation of head movement, and explore its application to resolve target ambiguity. The method estimates gaze depth by comparing the rotations of the eye and the head when the users look at a target and deliberately rotate their head. We show that VOR eye movement presents an alternative to vergence for gaze depth estimation, that is feasible also with monocular tracking. In an evaluation of its use for target disambiguation, our method outperforms vergence for targets presented at greater depth.
KW - VOR
KW - vestibular system
KW - eye tracking
KW - virtual reality
M3 - Conference contribution/Paper
SN - 9781450359702
BT - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
PB - ACM
T2 - 2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019
Y2 - 4 May 2019 through 9 May 2019
ER -