Event-based eye tracking is a promising solution for efficient and low-power eye tracking in smart eyewear technologies. However, the novelty of event-based sensors has resulted in a limited number of available datasets, particularly those with eye-level annotations, crucial for algorithm validation and deep-learning training. This paper addresses this gap by presenting an improved version of a popular event-based eye-tracking dataset. We introduce a semi-automatic annotation pipeline specifically designed for event-based data annotation. Additionally, we provide the scientific community with the computed annotations for pupil detection at 200Hz.
@article{arxiv.2502.03057,
title = {High-frequency near-eye ground truth for event-based eye tracking},
author = {Andrea Simpsi and Andrea Aspesi and Simone Mentasti and Luca Merigo and Tommaso Ongarello and Matteo Matteucci},
journal= {arXiv preprint arXiv:2502.03057},
year = {2025}
}