Online Recognition of Fixations, Saccades, and Smooth Pursuits for Automated Analysis of Traffic Hazard Perception

  • Enkelejda Kasneci
  • Gjergji Kasneci
  • Thomas C. Kübler
  • Wolfgang Rosenstiel
Conference paper

DOI: 10.1007/978-3-319-09903-3_20

Part of the Springer Series in Bio-/Neuroinformatics book series (SSBN, volume 4)
Cite this paper as:
Kasneci E., Kasneci G., Kübler T.C., Rosenstiel W. (2015) Online Recognition of Fixations, Saccades, and Smooth Pursuits for Automated Analysis of Traffic Hazard Perception. In: Koprinkova-Hristova P., Mladenov V., Kasabov N. (eds) Artificial Neural Networks. Springer Series in Bio-/Neuroinformatics, vol 4. Springer, Cham

Abstract

Complex and hazardous driving situations often arise with the delayed perception of traffic objects. To automatically detect whether such objects have been perceived by the driver, there is a need for techniques that can reliably recognize whether the driver’s eyes have fixated or are pursuing the hazardous object. A prerequisite for such techniques is the reliable recognition of fixations, saccades, and smooth pursuits from raw eye tracking data. This chapter addresses the challenge of analyzing the driver’s visual behavior in an adaptive and online fashion to automatically distinguish between fixation clusters, saccades, and smooth pursuits.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Enkelejda Kasneci
    • 1
  • Gjergji Kasneci
    • 2
  • Thomas C. Kübler
    • 1
  • Wolfgang Rosenstiel
    • 1
  1. 1.Department of Computer EngineeringUniversity of TübingenTübingenGermany
  2. 2.Hasso Plattner InstitutePotsdamGermany

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