通过二次斑点模式分析追踪微注视

Ola Shteinberg, Sergey Agdarov, Yafim Beiderman, Yoram S Bonneh, Inbal Ziv, Zeev Zalevsky
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摘要

在这里,我们提出了一种不依赖瞳孔的微注视跟踪技术和一种新颖的检测方法。我们提出了一种利用非接触式激光光子系统检测微注视的概念验证,该系统记录并处理从眼睛巩膜散射出的斑点图案的时间变化。数据由基于斑点的跟踪器(SBT)和基于视频的眼球跟踪器(Eyelink)同时记录,并通过常用的恩格伯特和克里格尔(E&K)检测方法以及先进的机器学习检测(MLD)技术进行分析。在 Eyelink 数据中使用 E&K 方法检测到的微注视中,我们在 SBT 数据中检测到了 93% 的微注视。通过使用 MLD,精确度达到了 86%。我们的研究结果表明,使用基于斑点的眼动仪测量微小的眼球运动(如微注视)具有潜在的改进空间,因此可以替代基于视频的眼动仪来检测微注视。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Microsaccades Tracking by Secondary Speckle Pattern Analysis.

Here we propose a not pupil-dependent microsaccades tracking technique and a novel detection method. We present a proof of concept for detecting microsaccades using a non-contact laser-based photonic system recording and processing the temporal changes of speckle patterns scattered from an eye sclera. The data, simultaneously recorded by the speckle-based tracker (SBT) and the video-based eye tracker (Eyelink), was analyzed by the frequently used detection method of Engbert and Kliegl (E&K) and by advanced machine learning detection (MLD) techniques. We detected 93% of microsaccades in the SBT data out of microsaccades detected in the Eyelink data with the E&K method. By utilizing MLD, a precision of 86% was achieved. The findings of our study demonstrate a potential improvement in measuring tiny eye movements, such as microsaccades, using speckle-based eye tracking and, thus, an alternative to video-based eye tracking for detecting microsaccades.

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