Optical Gaze Tracking with Spatially-Sparse Single-Pixel Detectors

TL;DR

This paper introduces a novel gaze tracking method using spatially-sparse single-pixel detectors, achieving high accuracy with low power and cost, suitable for next-gen VR/AR displays.

Contribution

It presents a new approach replacing traditional cameras with photodiodes and LEDs, including simulation and prototype development demonstrating improved performance.

Findings

First prototype: 2.67° error at 400Hz, 16mW power.

Second prototype: 1.57° error at 250Hz, 800mW power.

Efficient gaze tracking with simplified hardware and machine learning.

Abstract

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{\deg} 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{\deg} at 250 Hz using 800 mW.