Real-Time Gaze Tracking with Event-Driven Eye Segmentation
Real-Time Gaze Tracking with Event-Driven Eye Segmentation
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DOI：
10.1109/vr51125.2022.00059
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Yu Feng;Nathan Goulding;Asif Khan;Hans Reyserhove;Yuhao Zhu
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文献类型：
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作者：
Yu Feng;Nathan Goulding;Asif Khan;Hans Reyserhove;Yuhao Zhu
凝视的跟踪越来越多地成为增强和虚拟现实的重要组成部分。本文提出了一种实时的眼注跟踪算法，平均而言，该算法在移动处理器上以30 Hz的速度运行，达到0.1°–0.5°的凝视准确性，同时仅需要30K参数，比最新的眼睛跟踪算法小的数量级。我们的算法的症结很高。仅处理凝视的ROI。准确的ROI预测，无需特殊硬件。
Gaze tracking is increasingly becoming an essential component in Augmented and Virtual Reality. Modern gaze tracking algorithms are heavyweight; they operate at most 5 Hz on mobile processors despite that near-eye cameras comfortably operate at a real-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 0.1°–0.5° gaze accuracies, all the while requiring only 30K parameters, one to two orders of magnitude smaller than state-of-the-art eye tracking algorithms. The crux of our algorithm is an Auto ROI mode, which continuously predicts 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.