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Development of Open-source Software and Gaze Data Repositories for Performance Evaluation of Eye Tracking Systems.

Anuradha Kar1, Peter Corcoran1

  • 1Department of Electrical & Electronic Engineering, National University of Ireland, H91 TK33 Galway, Ireland.

Vision (Basel, Switzerland)
|November 19, 2019
PubMed
Summary

New open-source tools and datasets improve eye gaze data quality evaluation. GazeVisual-Lib and a benchmark dataset address challenges in eye tracking accuracy and reliability for vision research.

Keywords:
code repositorydata qualityeye gazeeye trackersfixationsgaze datasetperformance evaluation

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Data Science

Background:

  • Eye tracking systems face challenges in accuracy due to variable conditions like head pose and user distance.
  • A lack of standardized open-source tools and datasets hinders quantitative evaluation and comparison of eye tracker performance.
  • Reliable eye gaze data is crucial for advancements in vision research and applications.

Purpose of the Study:

  • To present open-source tools and datasets for quantitative evaluation of eye gaze data quality.
  • To address the need for robust methods to assess eye tracker performance under various operating conditions.
  • To facilitate benchmark comparisons between different eye tracking systems.

Main Methods:

  • Development of GazeVisual-Lib, an open-source repository with algorithms, visualizations, and software for detailed eye gaze data analysis.
  • Creation and release of a new labeled eye gaze dataset collected across multiple user platforms and conditions.
  • Organization and presentation of these resources within open data repositories.

Main Results:

  • GazeVisual-Lib provides comprehensive tools for analyzing eye tracker performance and data quality.
  • The new dataset enables benchmark comparisons of gaze data from diverse eye tracking systems.
  • The presented resources offer solutions for evaluating eye gaze data quality and tracker accuracy.

Conclusions:

  • The developed open-source tools and datasets significantly enhance the quantitative evaluation of eye gaze data quality.
  • GazeVisual-Lib and the benchmark dataset are expected to improve the performance analysis and reliability of eye tracking systems.
  • These resources will aid researchers in understanding and mitigating the impact of operating conditions on eye tracking accuracy.