Advertisement

A Low-Cost Solution of Eye Movement Data Acquisition Based on Computer Vision

  • Haoshu Gu
  • Junming DuEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 527)

Abstract

This paper presents a method and idea based on the frame-type eye movement data acquisition platform using the pupillary reflexology and image recognition technology utilizing computer vision that relies on a much lower hardware cost than existing equipment to meet the requirements of eye movement equipment for general human factors engineering science research. The method is inexpensive, easy to implement, has good accuracy, and subjects are able to wear equipment and move. For the application perspective of this method, it can effectively help reduce the cost of eye movement data acquisition project. The volume and weight of the entire hardware system can be controlled within a good range and depends on the compilability of python programs; it can be quickly ported to different platforms so the entire set of experimental equipment can be brought out of the laboratory to complete a more extensive study in human factors engineering research.

Keywords

Frame-type eye movement data acquisition platform Lower hardware costs Human factor engineering Pupillary reflexology Image recognition technology 

Notes

Acknowledgements

We would like to acknowledge the National Natural Science Foundation of China for funding this research under project: driver distraction strategies research based on in-vehicle technologies using (project number 71601007).

References

  1. 1.
    Bian F, Jiang M, Zhang H (2009) Gaze tracking technology and its application. Chin J Ergon 15(1):48–52Google Scholar
  2. 2.
    Tong F, Engel SA (2001) Interocular rivalry revealed in the human cortical blind-spot representation. Nature 13(6):195–199CrossRefGoogle Scholar
  3. 3.
    Puttemans S, Goedemé T (2014) Tobcat project results: applying object categorisation techniques in real-life industrial cases. Dsp Valley Newsl 15:9Google Scholar
  4. 4.
    Zhang J, Yang X, Zhao R (2005) Human eye precise positioning method based on Hough transformation circle detection. Comput Eng Appl 41(27):43–44Google Scholar
  5. 5.
    Zhu G, Zhang R (2008) Circle detective method based on Hough circle detection. Comput Eng Des 29(6):1462–1464Google Scholar
  6. 6.
    Platt JA, Uy OM, Heller DN, Cotter RJ, Fenselau C (1988) Computer-based linear regression analysis of desorption mass spectra of microorganisms. Anal Chem 60(14):1415–1419CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Transportation Science and EngineeringBeiHang UniversityBeijingChina

Personalised recommendations