  In this paper, we address the intricate challenge of gaze vector prediction,
a pivotal task with applications ranging from human-computer interaction to
driver monitoring systems. Our innovative approach is designed for the
demanding setting of extremely low-light conditions, leveraging a novel
temporal event encoding scheme, and a dedicated neural network architecture.
The temporal encoding method seamlessly integrates Dynamic Vision Sensor (DVS)
events with grayscale guide frames, generating consecutively encoded images for
input into our neural network. This unique solution not only captures diverse
gaze responses from participants within the active age group but also
introduces a curated dataset tailored for low-light conditions. The encoded
temporal frames paired with our network showcase impressive spatial
localization and reliable gaze direction in their predictions. Achieving a
remarkable 100-pixel accuracy of 100%, our research underscores the potency of
our neural network to work with temporally consecutive encoded images for
precise gaze vector predictions in challenging low-light videos, contributing
to the advancement of gaze prediction technologies.
