Online classification of eye tracking data for automated analysis of traffic hazard perception

Enkelejda Tafaj; Thomas C. Kübler; Gjergji Kasneci; Wolfgang Rosenstiel; Martin Bogdan

doi:10.1007/978-3-642-40728-4_56

Online classification of eye tracking data for automated analysis of traffic hazard perception

Enkelejda Tafaj, Thomas C. Kübler, Gjergji Kasneci, Wolfgang Rosenstiel, Martin Bogdan

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

33 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 (i.e., detecting fixations, saccades, and smooth pursuits from raw eye tracking data). This paper presents a system for analyzing the driver's visual behavior based on an adaptive online algorithm for detecting and distinguishing between fixation clusters, saccades, and smooth pursuits.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings
Pages	442-450
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-40728-4_56
State	Published - 2013
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	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8131 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

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

Keywords

classification
eye data
perception
traffic hazard

Access to Document

10.1007/978-3-642-40728-4_56

Cite this

Tafaj, E., Kübler, T. C., Kasneci, G., Rosenstiel, W., & Bogdan, M. (2013). Online classification of eye tracking data for automated analysis of traffic hazard perception. In Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings (pp. 442-450). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8131 LNCS). https://doi.org/10.1007/978-3-642-40728-4_56

Tafaj, Enkelejda ; Kübler, Thomas C. ; Kasneci, Gjergji et al. / Online classification of eye tracking data for automated analysis of traffic hazard perception. Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings. 2013. pp. 442-450 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Online classification of eye tracking data 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 (i.e., detecting fixations, saccades, and smooth pursuits from raw eye tracking data). This paper presents a system for analyzing the driver's visual behavior based on an adaptive online algorithm for detecting and distinguishing between fixation clusters, saccades, and smooth pursuits.",

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

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "23rd International Conference on Artificial Neural Networks, ICANN 2013 ; Conference date: 10-09-2013 Through 13-09-2013",

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Tafaj, E, Kübler, TC, Kasneci, G, Rosenstiel, W & Bogdan, M 2013, Online classification of eye tracking data for automated analysis of traffic hazard perception. in Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8131 LNCS, pp. 442-450, 23rd International Conference on Artificial Neural Networks, ICANN 2013, Sofia, Bulgaria, 10/09/13. https://doi.org/10.1007/978-3-642-40728-4_56

Online classification of eye tracking data for automated analysis of traffic hazard perception. / Tafaj, Enkelejda; Kübler, Thomas C.; Kasneci, Gjergji et al.
Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings. 2013. p. 442-450 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8131 LNCS).

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

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AU - Rosenstiel, Wolfgang

AU - Bogdan, Martin

PY - 2013

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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 (i.e., detecting fixations, saccades, and smooth pursuits from raw eye tracking data). This paper presents a system for analyzing the driver's visual behavior based on an adaptive online algorithm for detecting and distinguishing 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 (i.e., detecting fixations, saccades, and smooth pursuits from raw eye tracking data). This paper presents a system for analyzing the driver's visual behavior based on an adaptive online algorithm for detecting and distinguishing between fixation clusters, saccades, and smooth pursuits.

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DO - 10.1007/978-3-642-40728-4_56

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings

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Tafaj E, Kübler TC, Kasneci G, Rosenstiel W, Bogdan M. Online classification of eye tracking data for automated analysis of traffic hazard perception. In Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings. 2013. p. 442-450. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-40728-4_56

Online classification of eye tracking data for automated analysis of traffic hazard perception

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Keywords

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