Open Framework for Error-Compensated Gaze Data Collection with Eye Tracking Glasses

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Eye tracking is nowadays the primary method for collecting training data for neural networks in the Human Visual System modelling. Our recommendation is to collect eye tracking data from videos with eye tracking glasses that are more affordable and applicable to diverse test conditions than conventionally used screen based eye trackers. Eye tracking glasses are prone to moving during the gaze data collection but our experiments show that the observed displacement error accumulates fairly linearly and can be compensated automatically by the proposed framework. This paper describes how our framework can be used in practice with videos up to 4K resolution. The proposed framework and the data collected during our sample experiment are made publicly available.
Original languageEnglish
Title of host publication2018 IEEE International Symposium on Multimedia (ISM)
Number of pages4
ISBN (Electronic)978-1-5386-6857-3
ISBN (Print)978-1-5386-6858-0
Publication statusPublished - Dec 2018
Publication typeA4 Article in conference proceedings
EventIEEE International Symposium on Multimedia - Taichung, Taiwan, Province of China
Duration: 10 Dec 201812 Dec 2018
Conference number: 20


ConferenceIEEE International Symposium on Multimedia
Abbreviated titleISM
Country/TerritoryTaiwan, Province of China


  • eye tracking
  • open-source
  • open data set
  • error correction

Publication forum classification

  • Publication forum level 1


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