2022/01/19 by Feng Yu, Nathan Goulding-Hotta, Feng, Yu +7 · 4 citations
Computer Science · Medicine · #Gaze Tracking and Assistive Technology #Retinal Imaging and Analysis #Visual Attention and Saliency Detection
paper · pdf · doi:10.48550/arxiv.2201.07367
Gaze tracking is increasingly becoming an essential component in Augmented and Virtual Reality. Modern gaze tracking al gorithms are heavyweight; they operate at most 5 Hz on mobile processors despite that near-eye cameras comfortably operate at a r eal-time rate (> 30 Hz). This paper presents a real-time eye tracking algorithm that, on average, operates at 30 Hz on a mobile processor, achieves \ang0.1--\ang0.5 gaze accuracies, all the while requiring only 30K parameters, one to two orders of magn itude smaller than state-of-the-art eye tracking algorithms. The crux of our algorithm is an Auto~ROI mode, which continuously pr edicts the Regions of Interest (ROIs) of near-eye images and judiciously processes only the ROIs for gaze estimation. To that end, we introduce a novel, lightweight ROI prediction algorithm by emulating an event camera. We discuss how a software emulation of events enables accurate ROI prediction without requiring special hardware. The code of our paper is available at https://github.com/horizon-research/edgaze.