Combining Gaze Estimation and Optical Flow for Pursuits Interaction
Mihai Bâce,
Vincent Becker,
Chenyang Wang,
Andreas Bulling
Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA),
pp. 1-10,
2020.
Abstract
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Best Paper Award
Pursuit eye movements have become widely popular because they enable spontaneous eye-based interaction. However, existing methods to detect smooth pursuits require special-purpose eye trackers. We propose the first method to detect pursuits using a single off-the-shelf RGB camera in unconstrained remote settings. The key novelty of our method is that it combines appearance-based gaze estimation with optical flow in the eye region to jointly analyse eye movement dynamics in a single pipeline. We evaluate the performance and robustness of our method for different numbers of targets and trajectories in a 13-participant user study. We show that our method not only outperforms the current state of the art but also achieves competitive performance to a consumer eye tracker for a small number of targets. As such, our work points towards a new family of methods for pursuit interaction directly applicable to an ever-increasing number of devices readily equipped with cameras.
@inproceedings{bace20_etra,
title = {Combining Gaze Estimation and Optical Flow for Pursuits Interaction},
author = {B{\^a}ce, Mihai and Becker, Vincent and Wang, Chenyang and Bulling, Andreas},
year = {2020},
booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)},
doi = {10.1145/3379155.3391315},
pages = {1-10}
}
