论文标题
通过事件驱动的眼球细分实时凝视跟踪
Real-Time Gaze Tracking with Event-Driven Eye Segmentation
论文作者
论文摘要
凝视跟踪越来越成为增强和虚拟现实中的重要组成部分。现代凝视跟踪Al Gorithm是重量级人物；他们在移动处理器上最多可以运行5 Hz，尽管近眼相机以频率的速度运行（$> $ 30 Hz）。本文提出了一种实时的眼动追踪算法，平均而言，该算法在移动处理器上以30 Hz的形式运行\ ang {0.1} - \ ang {0.5}凝视精度，而同时只需30k参数，一到两个巨大的巨大速度比那样的宏伟的巨大的巨大痕迹追踪型号追踪型号。我们算法的症结是一种自动ROI模式，它不断地统治着近眼图像的感兴趣区域（ROI），并且明智地处理了ROI的凝视估计。为此，我们通过模拟事件摄像头介绍了一种小说，轻量级的ROI预测算法。我们讨论事件的软件仿真如何实现无需特殊硬件的准确ROI预测。我们论文的代码可从https://github.com/horizon-research/edgaze获得。
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 \ang{0.1}--\ang{0.5} 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.
