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Directional heat map generation with saccade information

Published:04 December 2018Publication History

ABSTRACT

Heat map is an important tool for eye tracking data analysis and visualization. It is very intuitive to express the area watched by the observer, but conventional methods ignore saccade information that express gaze shifts. Based on the conventional method, this paper presents a novel heat map generation method for eye tracking data. Proposed method uses a mixed data structure of fixation points and saccades, and also adds heat map deformation calculation with saccade type data. The proposed method has advantages on indicating gaze transition path while also visualizing gaze region. Related applications will benefit from this work.

References

  1. Tanja Blascheck, Kuno Kurzhals, Michael Raschke, Michael Burch, Daniel Weiskopf, and Thomas Ertl. 2017. Visualization of eye tracking data: A taxonomy and survey. In Computer Graphics Forum, Vol. 36. Wiley Online Library, 260--284.Google ScholarGoogle Scholar
  2. Pieter Blignaut. 2010. Visual Span and Other Parameters for the Generation of Heatmaps. In Proceedings of the 2010 Symposium on Eye-Tracking Research; Applications (ETRA '10). ACM, New York, NY, USA, 125--128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Michael Burch. 2017. Mining and Visualizing Eye Movement Data. In SIGGRAPH Asia 2017 Symposium on Visualization (SA '17). ACM, New York, NY, USA, Article 3, 8 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Oleg Špakov and Darius Miniotas. 2007. Visualization of eye gaze data using heat maps. Elektronika ir elektrotechnika 74, 2 (2007), 55--58.Google ScholarGoogle Scholar
  5. Andrew T. Duchowski. 2017. Eye Tracking Methodology.Google ScholarGoogle Scholar

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  1. Directional heat map generation with saccade information

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    • Published in

      cover image ACM Conferences
      SA '18: SIGGRAPH Asia 2018 Posters
      December 2018
      166 pages
      ISBN:9781450360630
      DOI:10.1145/3283289

      Copyright © 2018 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 4 December 2018

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      Acceptance Rates

      Overall Acceptance Rate178of869submissions,20%

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