  Saccades are extremely rapid movements of both eyes that occur
simultaneously, typically observed when an individual shifts their focus from
one object to another. These movements are among the swiftest produced by
humans and possess the potential to achieve velocities greater than that of
blinks. The peak angular speed of the eye during a saccade can reach as high as
700{\deg}/s in humans, especially during larger saccades that cover a visual
angle of 25{\deg}. Previous research has demonstrated encouraging outcomes in
comprehending neurological conditions through the study of saccades. A
necessary step in saccade detection involves accurately identifying the precise
location of the pupil within the eye, from which additional information such as
gaze angles can be inferred. Conventional frame-based cameras often struggle
with the high temporal precision necessary for tracking very fast movements,
resulting in motion blur and latency issues. Event cameras, on the other hand,
offer a promising alternative by recording changes in the visual scene
asynchronously and providing high temporal resolution and low latency. By
bridging the gap between traditional computer vision and event-based vision, we
present events as frames that can be readily utilized by standard deep learning
algorithms. This approach harnesses YOLOv8, a state-of-the-art object detection
technology, to process these frames for pupil tracking using the publicly
accessible Ev-Eye dataset. Experimental results demonstrate the framework's
effectiveness, highlighting its potential applications in neuroscience,
ophthalmology, and human-computer interaction.
