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Embedded CPU-GPU pupil tracking.

Bartlomiej Kowalski1, Xiaojing Huang1, Alfredo Dubra1

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

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|December 16, 2024
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Summary
This summary is machine-generated.

This study demonstrates camera-based pupil tracking with low latency on standard computers, achieving high precision without ethnic bias. The method uses high-level programming for accessible, efficient eye-tracking applications.

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Area of Science:

  • Biomedical Optics
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Specialized hardware and programming previously achieved low latencies for camera-based pupil tracking.
  • High-level programming on standard computing platforms offers potential for more accessible pupil tracking systems.

Purpose of the Study:

  • To explore camera-based pupil tracking using high-level programming on consumer-grade central processing units (CPUs) and graphics processing units (GPUs).
  • To achieve low calculation latencies comparable to specialized systems.
  • To assess the system's performance, precision, and potential ethnic bias.

Main Methods:

  • Utilized two Scheimpflug optical setups with telecentric optics and 940 nm illumination.
  • Tested various desktop and embedded computers with different operating systems.
  • Employed complementary metal-oxide-semiconductor cameras with global shutter at high frame rates (up to 1,897 fps) and varying regions of interest (ROIs).

Main Results:

  • Achieved tracking precision of approximately 0.9-4.4 µm at safe light levels.
  • Demonstrated low calculation times: as low as 0.5 ms on desktop computers and 0.8-1.3 ms on embedded computers.
  • Eye images from diverse ethnic groups showed no discernible bias in tracking performance.

Conclusions:

  • Camera-based pupil tracking is feasible with high-level programming on standard computing platforms, achieving low latencies and high precision.
  • The developed system is accessible, potentially free from ethnic bias, and suitable for integration with other instruments.
  • This approach democratizes advanced eye-tracking technology, moving beyond specialized hardware requirements.