High-frequency near-eye ground truth for event-based eye tracking
This work addresses a data gap for researchers developing eye-tracking algorithms in smart eyewear, but it is incremental as it builds on an existing dataset.
The paper tackled the lack of annotated datasets for event-based eye tracking by presenting an improved dataset with a semi-automatic annotation pipeline, providing pupil detection annotations at 200Hz.
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.