Abstract
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.
Abstract (translated)
凝视估计是一种有价值的技术，在诸如人机交互、虚拟现实和医疗等领域具有众多应用。本报告使用索尼Spresense微控制器板实现了凝视估计系统的部署，并探讨了其在延迟、MAC循环和功耗方面的性能。报告还提供了系统架构的见解，包括使用的凝视估计模型。此外，系统功能和性能的演示也被呈现出来。我们的轻量级模型TinyTrackerS仅占169Kb，使用85.8k个参数，并运行在Spresense平台上，采样率为3 FPS。
URL
https://arxiv.org/abs/2308.12313
