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.

Best Paper Award

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We propose a novel method to detect pursuits using a single off-the-shelf RGB camera. Our method jointly analyses the eye gaze direction and optical flow in the eye region to identify the target the user is following.

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Abstract

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.

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