Online recognition of fixations, saccades, and smooth pursuits for automated analysis of traffic hazard perception

Enkelejda Kasneci; Gjergji Kasneci; Thomas C. Kübler; Wolfgang Rosenstiel

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

Online recognition of fixations, saccades, and smooth pursuits for automated analysis of traffic hazard perception

Enkelejda Kasneci, Gjergji Kasneci, Thomas C. Kübler, Wolfgang Rosenstiel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

40 Scopus citations

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.

Original language	English
Title of host publication	Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics
Publisher	Springer Verlag
Pages	411-434
Number of pages	24
ISBN (Print)	9783319099026
DOIs	https://doi.org/10.1007/978-3-319-09903-3_20
State	Published - 2015
Externally published	Yes
Event	23rd International Conference on Artificial Neural Networks, ICANN 2013 - Sofia, Bulgaria Duration: 10 Sep 2013 → 13 Sep 2013

Publication series

Name	Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics

Conference

Conference	23rd International Conference on Artificial Neural Networks, ICANN 2013
Country/Territory	Bulgaria
City	Sofia
Period	10/09/13 → 13/09/13

Access to Document

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

Cite this

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 Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics (pp. 411-434). (Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics). Springer Verlag. https://doi.org/10.1007/978-3-319-09903-3_20

Kasneci, Enkelejda ; Kasneci, Gjergji ; Kübler, Thomas C. et al. / Online recognition of fixations, saccades, and smooth pursuits for automated analysis of traffic hazard perception. Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics. Springer Verlag, 2015. pp. 411-434 (Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics).

@inproceedings{8359a2d96ea347e693d578a0e089773d,

title = "Online recognition of fixations, saccades, and smooth pursuits for automated analysis of traffic hazard perception",

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.",

author = "Enkelejda Kasneci and Gjergji Kasneci and K{\"u}bler, {Thomas C.} and Wolfgang Rosenstiel",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing 2015.; 23rd International Conference on Artificial Neural Networks, ICANN 2013 ; Conference date: 10-09-2013 Through 13-09-2013",

year = "2015",

doi = "10.1007/978-3-319-09903-3_20",

language = "English",

isbn = "9783319099026",

series = "Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics",

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Kasneci, E , Kasneci, G, Kübler, TC & Rosenstiel, W 2015, Online recognition of fixations, saccades, and smooth pursuits for automated analysis of traffic hazard perception. in Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics. Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics, Springer Verlag, pp. 411-434, 23rd International Conference on Artificial Neural Networks, ICANN 2013, Sofia, Bulgaria, 10/09/13. https://doi.org/10.1007/978-3-319-09903-3_20

Online recognition of fixations, saccades, and smooth pursuits for automated analysis of traffic hazard perception. / Kasneci, Enkelejda ; Kasneci, Gjergji; Kübler, Thomas C. et al.
Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics. Springer Verlag, 2015. p. 411-434 (Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Online recognition of fixations, saccades, and smooth pursuits for automated analysis of traffic hazard perception

AU - Kasneci, Enkelejda

AU - Kasneci, Gjergji

AU - Kübler, Thomas C.

AU - Rosenstiel, Wolfgang

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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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U2 - 10.1007/978-3-319-09903-3_20

DO - 10.1007/978-3-319-09903-3_20

M3 - Conference contribution

AN - SCOPUS:85008388752

SN - 9783319099026

T3 - Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics

SP - 411

EP - 434

BT - Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics

PB - Springer Verlag

T2 - 23rd International Conference on Artificial Neural Networks, ICANN 2013

Y2 - 10 September 2013 through 13 September 2013

ER -

Kasneci E , Kasneci G, Kübler TC, Rosenstiel W. Online recognition of fixations, saccades, and smooth pursuits for automated analysis of traffic hazard perception. In Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics. Springer Verlag. 2015. p. 411-434. (Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics). doi: 10.1007/978-3-319-09903-3_20

Online recognition of fixations, saccades, and smooth pursuits for automated analysis of traffic hazard perception

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