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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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基于模型的3D凝视估计使用TOF摄像机.

Kuanxin Shen1, Yingshun Li2, Zhannan Guo2

  • 1School of Chemical Process Automation, Shenyang University of Technology, Liaoyang 111003, China.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
概括

这项研究介绍了一种基于模型的新目光估计算法,使用低分辨率的3D飞行时间 (TOF) 摄像头和红外图像. 该方法准确地估计了视角,即使在具有挑战性的驾驶环境中.

关键词:
在TOF摄像机上.YOLOv8神经网络的神经网络在驾驶员的眼睛追踪.低分辨率的红外图像.基于模型的凝视估计估计.

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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:

  • 使用RGB图像和CNN的基于外观的目光估计方法在不同的照明下扎.
  • 基于模型的方法需要高分辨率的图像,限制了实际应用.
  • 现有的技术在户外和现实世界中面临挑战.

研究的目的:

  • 为充满挑战的环境提出基于模型的强大视线估计算法.
  • 通过利用红外图像来克服对照明的依赖.
  • 为了使用低分辨率的3D飞行时间 (TOF) 摄像头进行准确的目光估计.

主要方法:

  • 使用训练有素的YOLOv8神经网络来检测眼睛的地标.
  • 从TOF摄像机集成的深度信息来计算3Dcanthus坐标.
  • 开发了一个3D几何眼球模型来确定视角.

主要成果:

  • 获得了6.03° (水平) 和4.83° (垂直) 的根平均平方误差,用于视角估计.
  • 在真实的汽车驾驶环境中,证明了稳定的驾驶员目光检测.
  • 在汽车中的各种位置 (如仪表板和镜子) 验证了性能.

结论:

  • 拟议的方法提供了一个可靠的解决方案,用于在多样化和具有挑战性的条件下对目光进行估计.
  • 红外成像和3D TOF数据增强了对照明变化的强度.
  • 该算法显示了驾驶员监控和人机交互系统的巨大潜力.