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嵌入式的CPU-GPU追踪学生跟踪

Bartlomiej Kowalski1, Xiaojing Huang1, Alfredo Dubra1

  • 1Department of Ophthalmology, Stanford University, Palo Alto, CA 94303, USA.

Biomedical optics express
|December 16, 2024
PubMed
概括
此摘要是机器生成的。

这项研究表明,在标准计算机上,以低延迟的速度进行基于摄像头的学生跟踪,在没有种族偏见的情况下实现高精度. 该方法使用高级编程来实现可访问,高效的眼睛跟踪应用程序.

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科学领域:

  • 生物医学光学 生物医学光学
  • 计算机视觉 计算机视觉
  • 人与计算机的交互

背景情况:

  • 专用硬件和编程以前实现了基于摄像头的学生跟踪的低延迟.
  • 在标准计算平台上的高级编程为更容易访问的学生跟踪系统提供了潜力.

研究的目的:

  • 探索基于摄像头的学生跟踪,使用消费级中央处理单元 (CPU) 和图形处理单元 (GPU) 的高级编程.
  • 为了实现与专业系统相比较的低计算延迟.
  • 评估系统的性能,精度和潜在的种族偏见.

主要方法:

  • 使用了两个 Scheimpflug 光学设置,具有远程中心光学和 940 nm 照明.
  • 测试了各种不同操作系统的桌面和嵌入式计算机.
  • 采用了配套的金属氧化物半导体摄像头,在高率 (高达1897/秒) 和不同感兴趣区域 (ROI) 的全球快门.

主要成果:

  • 在安全的光线水平下,实现了大约0.9-4.4微米的追踪精度.
  • 证明了低计算时间:在桌面计算机上低至0.5ms,在嵌入式计算机上低至0.8-1.3ms.
  • 来自不同种族群体的眼睛图像在跟踪表现方面没有明显的偏见.

结论:

  • 基于摄像头的学生跟踪在标准计算平台上的高级编程是可行的,实现低延迟和高精度.
  • 开发的系统是可访问的,可能没有种族偏见,并且适合与其他工具集成.
  • 这种方法使先进的眼睛追踪技术民主化,超越了专门的硬件要求.