Optical Gaze Tracking with Spatially-Sparse Single-Pixel Detectors
This addresses power, cost, and accuracy issues in gaze tracking for next-generation displays, though it appears incremental as it builds on existing hardware and machine learning methods.
The paper tackled the problem of gaze tracking for VR/AR displays by proposing a system using discrete photodiodes and LEDs instead of cameras, achieving an average error rate of 1.57° at 250 Hz with 800 mW in their second prototype.
Gaze tracking is an essential component of next generation displays for virtual reality and augmented reality applications. Traditional camera-based gaze trackers used in next generation displays are known to be lacking in one or multiple of the following metrics: power consumption, cost, computational complexity, estimation accuracy, latency, and form-factor. We propose the use of discrete photodiodes and light-emitting diodes (LEDs) as an alternative to traditional camera-based gaze tracking approaches while taking all of these metrics into consideration. We begin by developing a rendering-based simulation framework for understanding the relationship between light sources and a virtual model eyeball. Findings from this framework are used for the placement of LEDs and photodiodes. Our first prototype uses a neural network to obtain an average error rate of 2.67° at 400Hz while demanding only 16mW. By simplifying the implementation to using only LEDs, duplexed as light transceivers, and more minimal machine learning model, namely a light-weight supervised Gaussian process regression algorithm, we show that our second prototype is capable of an average error rate of 1.57° at 250 Hz using 800 mW.