research-article

The applicability of probabilistic methods to the online recognition of fixations and saccades in dynamic scenes

Authors:

Enkelejda Kasneci,

Gjergji Kasneci,

Thomas C. Kübler,

Wolfgang RosenstielAuthors Info & Claims

ETRA '14: Proceedings of the Symposium on Eye Tracking Research and Applications

Pages 323 - 326

https://doi.org/10.1145/2578153.2578213

Published: 26 March 2014 Publication History

Abstract

In many applications involving scanpath analysis, especially when dynamic scenes are viewed, consecutive fixations and saccades, have to be identified and extracted from raw eye-tracking data in an online fashion. Since probabilistic methods can adapt not only to the individual viewing behavior, but also to changes in the scene, they are best suited for such tasks.

In this paper we analyze the applicability of two types of main-stream probabilistic models to the identification of fixations and saccades in dynamic scenes: (1) Hidden Markov Models and (2) Bayesian Online Mixture Models. We analyze and compare the classification performance of the models on eye-tracking data collected during real-world driving experiments.

References

[1]

Berger, C., Winkels, M., Lischke, A., and Höppner, J. 2011. GazeAlyze: a MATLAB toolbox for the analysis of eye movement data. Behavioral Research Methods 44, 2, 1--16.

[2]

Blignaut, P. 2009. Fixation identification: The optimum threshold for a dispersion algorithm. Attention, Perception, & Psychophysics 71, 4, 881--895.

[3]

Forney Jr., G. D. 1973. The viterbi algorithm. Proceedings of the IEEE 61, 3, 268--278.

[4]

Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., and Van de Weijer, J. 2011. Eye tracking: A comprehensive guide to methods and measures. Oxford University Press.

[5]

Kasneci, E., Sippel, K., Aehling, K., Heister, M., Rosenstiel, W., Schiefer, U., and Papageorgiou, E. 2014. Driving with Binocular Visual Field Loss? A Study on a Supervised On-road Parcours with Simultaneous Eye and Head Tracking. Plos One.

[6]

Kasneci, E. 2013. Towards the Automated Recognition of Assistance Need for Drivers with Impaired Visual Field. PhD thesis, University of Tübingen, Wilhelmstr. 32, 72074 Tübingen.

[7]

Komogortsev, O. V., and Karpov, A. 2013. Automated classification and scoring of smooth pursuit eye movements in the presence of fixations and saccades. Behavior Research Methods 45, 1, 203--215.

[8]

Komogortsev, O. V., Jayarathna, S., Koh, D. H., and Gowda, S. M. 2010. Qualitative and quantitative scoring and evaluation of the eye movement classification algorithms. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, ACM, New York, NY, USA, ETRA '10, 65--68.

Digital Library

[9]

Leigh, R. J., and Zee, D. S. 2006. The neurology of eye movements. Oxford University Press.

[10]

Privitera, C. M., and Stark, L. W. 2005. Scanpath theory, attention, and image processing algorithms for predicting human eye fixations. In Neurobiology of Attention, L. Itti, G. Rees, and J. Tsotsos, Eds., 269--299.

[11]

Salvucci, D., and Goldberg, J. 2000. Identifying fixations and saccades in eye-tracking protocols. In Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, ACM, New York, NY, USA, ETRA '00, 71--78.

Digital Library

[12]

Santella, A., and DeCarlo, D. 2004. Robust clustering of eye movement recordings for quantification of visual interest. In Proceedings of the 2004 Symposium on Eye Tracking Research & Applications, ACM, New York, NY, USA, ETRA '04, 27--34.

Digital Library

[13]

Sauter, D., Martin, B. J., Di Renzo, N., and Vomscheid, C. 1991. Analysis of eye tracking movements using innovations generated by a Kalman filter. Medical and biological Engineering and Computing 29, 1, 63--69.

[14]

Shic, F., Scassellati, B., and Chawarska, K. 2008. The incomplete fixation measure. In Proceedings of the 2008 Symposium on Eye Tracking Research & Applications, ACM, New York, NY, USA, ETRA '08, 111--114.

Digital Library

[15]

Tafaj, E., Kübler, T., Peter, J., Schiefer, U., Bogdan, M., and Rosenstiel, W. 2011. Vishnoo - an open-source software for vision research. In Proceedings of the 24^th IEEE International Symposium on Computer-Based Medical Systems, IEEE, CBMS' 11, 1--6.

Digital Library

[16]

Tafaj, E., Kasneci, G., Rosenstiel, W., and Bogdan, M. 2012. Bayesian online clustering of eye movement data. In Proceedings of the Symposium on Eye Tracking Research and Applications, ACM, New York, NY, USA, ETRA '12, 285--288.

Digital Library

[17]

Tafaj, E., Kübler, T., Kasneci, G., Rosenstiel, W., and 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, Springer Berlin Heidelberg, vol. 8131, 442--450.

[18]

Tafaj, E., Hempel, S., Heister, M., Aehling, K., Schaeffel, F., Dietzsch, J., Rosenstiel, W., and Schiefer, U. 2013. A New Method for Assessing the Exploratory Field of View (EFOV). In HEALTHINF 2012, SciTePress, D. Stacey, J. Sol-Casals, A. L. N. Fred, and H. Gamboa, Eds., 5--11.

[19]

Urruty, T., Lew, S., Ihadaddene, N., and Simovici, D. A. 2007. Detecting eye fixations by projection clustering. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) 3, 4, 1--20.

Digital Library

Cited By

Gavas RRoy SChatterjee DTripathy SChakravarty KSinha A(2018)Enhancing the usability of low-cost eye trackers for rehabilitation applicationsPLOS ONE10.1371/journal.pone.019634813:6(e0196348)Online publication date: 1-Jun-2018
https://doi.org/10.1371/journal.pone.0196348
Kasneci EKbler TBroelemann KKasneci G(2017)Aggregating physiological and eye tracking signals to predict perception in the absence of ground truthComputers in Human Behavior10.1016/j.chb.2016.11.06768:C(450-455)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.chb.2016.11.067
Kübler TFuhl WRosenberg RRosenstiel WKasneci E(2016)Novel Methods for Analysis and Visualization of Saccade TrajectoriesComputer Vision – ECCV 2016 Workshops10.1007/978-3-319-46604-0_54(783-797)Online publication date: 18-Sep-2016
https://doi.org/10.1007/978-3-319-46604-0_54
Show More Cited By

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The applicability of probabilistic methods to the online recognition of fixations and saccades in dynamic scenes

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cover image ACM Conferences

ETRA '14: Proceedings of the Symposium on Eye Tracking Research and Applications

March 2014

394 pages

ISBN:9781450327510

DOI:10.1145/2578153

Conference Chairs:
Pernilla Qvarfordt
FX Palo Alto Laboratory
,
Dan Witzner Hansen
IT University, Denmark

Copyright © 2014 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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Published: 26 March 2014

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ETRA '14

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ETRA '14: Eye Tracking Research and Applications

March 26 - 28, 2014

Florida, Safety Harbor

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Overall Acceptance Rate 69 of 137 submissions, 50%

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Cited By

Gavas RRoy SChatterjee DTripathy SChakravarty KSinha A(2018)Enhancing the usability of low-cost eye trackers for rehabilitation applicationsPLOS ONE10.1371/journal.pone.019634813:6(e0196348)Online publication date: 1-Jun-2018
https://doi.org/10.1371/journal.pone.0196348
Kasneci EKbler TBroelemann KKasneci G(2017)Aggregating physiological and eye tracking signals to predict perception in the absence of ground truthComputers in Human Behavior10.1016/j.chb.2016.11.06768:C(450-455)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.chb.2016.11.067
Kübler TFuhl WRosenberg RRosenstiel WKasneci E(2016)Novel Methods for Analysis and Visualization of Saccade TrajectoriesComputer Vision – ECCV 2016 Workshops10.1007/978-3-319-46604-0_54(783-797)Online publication date: 18-Sep-2016
https://doi.org/10.1007/978-3-319-46604-0_54
Kübler TSippel KFuhl WSchievelbein GAufreiter JRosenberg RRosenstiel WKasneci E(2016)Analysis of Eye Movements with EyetraceBiomedical Engineering Systems and Technologies10.1007/978-3-319-27707-3_28(458-471)Online publication date: 5-Jan-2016
https://doi.org/10.1007/978-3-319-27707-3_28
Kasneci EKasneci GKübler TRosenstiel W(2015)Online Recognition of Fixations, Saccades, and Smooth Pursuits for Automated Analysis of Traffic Hazard PerceptionArtificial Neural Networks10.1007/978-3-319-09903-3_20(411-434)Online publication date: 2015
https://doi.org/10.1007/978-3-319-09903-3_20
Sippel KKasneci EAehling KHeister MRosenstiel WSchiefer UPapageorgiou E(2014)Binocular Glaucomatous Visual Field Loss and Its Impact on Visual Exploration - A Supermarket StudyPLoS ONE10.1371/journal.pone.01060899:8(e106089)Online publication date: 27-Aug-2014
https://doi.org/10.1371/journal.pone.0106089
Kasneci ESippel KAehling KHeister MRosenstiel WSchiefer UPapageorgiou E(2014)Driving with Binocular Visual Field Loss? A Study on a Supervised On-Road Parcours with Simultaneous Eye and Head TrackingPLoS ONE10.1371/journal.pone.00874709:2(e87470)Online publication date: 11-Feb-2014
https://doi.org/10.1371/journal.pone.0087470
Kubler TBukenberger DUngewiss JWorner ARothe CSchiefer URosenstiel WKasneci E(2014)Towards automated comparison of eye-tracking recordings in dynamic scenes2014 5th European Workshop on Visual Information Processing (EUVIP)10.1109/EUVIP.2014.7018371(1-6)Online publication date: Dec-2014
https://doi.org/10.1109/EUVIP.2014.7018371

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