Gaze estimation is a valuable technology with numerous applications in fields such as human-computer interaction, virtual reality, and medicine. This report presents the implementation of a gaze estimation system using the Sony Spresense microcontroller board and explores its performance in latency, MAC/cycle, and power consumption. The report also provides insights into the system's architecture, including the gaze estimation model used. Additionally, a demonstration of the system is presented, showcasing its functionality and performance. Our lightweight model TinyTrackerS is a mere 169Kb in size, using 85.8k parameters and runs on the Spresense platform at 3 FPS.
Cite
@article{arxiv.2308.12313,
title = {Gaze Estimation on Spresense},
author = {Thomas Ruegg and Pietro Bonazzi and Andrea Ronco},
journal= {arXiv preprint arXiv:2308.12313},
year = {2023}
}