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使用深度学习来提高视线跟踪在虚拟现实中的稳定性,准确性和精度

Kevin Barkevich¹, Reynold Bailey¹, Gabriel J Diaz¹

¹Rochester Institute of Technology, USA.

Proceedings of the ACM on computer graphics and interactive techniques

|August 9, 2024

概括

机器学习眼睛跟踪可以改善瞳孔跟踪,但可能会影响视线估计的准确性. 本研究客观地评估了ML方法.

科学领域:

计算机视觉计算机视觉
人与计算机的交互
生物医学工程生物医学工程

背景情况:

凝视估计算法依赖于跟踪眼睛的特征,如瞳孔.
传统的计算机视觉方法与遮蔽和反射作斗争.
机器学习 (ML) 显示了改善学生跟踪的希望.

研究的目的:

客观地评估基于ML的学生跟踪对目光估计质量的影响.
将ML方法与视线估计中的传统技术进行比较.
为了评估凝视估计的准确性,精度和脱落率.

主要方法:

实施和评估了几种基于ML的当代眼睛追踪方法.
评估了ML跟踪对基于特征和基于模型的目光估计的影响.
测量了凝视估计的准确性,精度和失学率.

主要成果:

基于ML的学生跟踪可以影响最终的目光估计质量.
客观指标揭示了性能变化,这取决于ML方法和目光估计方法.
对比强调了细分性能和凝视精度之间的权衡.

结论:

机器学习方法为眼睛追踪器提供了瞳孔追踪方面的进步.
需要仔细评估,以了解ML对目光估计的下游影响.
这项研究为开发强大的眼睛追踪系统提供了关键的见解.

关键词:

眼睛跟踪眼睛跟踪凝视的估计估计.神经网络的神经网络的神经网络虚拟现实虚拟现实虚拟现实虚拟现实

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