
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.
AbstractLinksBibTeXProject 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 = {Mihai B{\^a}ce and Vincent Becker and Chenyang Wang and Andreas Bulling},
year = {2020},
booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)},
pages = {1-10},
doi = {10.1145/3379155.3391315},
}