short-paper

Predicting eye movements in multiple object tracking using neural networks

Authors:

Filip Dechterenko,

Jiri LukavskyAuthors Info & Claims

ETRA '16: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications

Pages 271 - 274

https://doi.org/10.1145/2857491.2857502

Published: 14 March 2016 Publication History

Abstract

In typical Multiple Object Tracking (MOT) paradigm, the participant's task is to track targets amongst distractors for several seconds. Understanding gaze strategies in MOT can help us reveal attentional mechanisms in dynamic tasks. Previous attempts relied on analytical strategies (such as averaging object positions). An alternative approach is to find this relationship using machine learning technique. After preprocessing, we assembled a dataset with 48,000 datapoints, representing 1534 MOT trials or 2.5 hours. In this study, we used feedforward neural networks to predict gaze position and compared predicted gaze with analytical strategies from previous studies using median distance. Our results showed that neural networks were able to predict eye positions better than current strategies. Particularly, they performed better when we trained the network with all objects, not targets only. It supports the hypothesis that people are influenced by distractor positions during tracking.

References

[1]

Bouma, H. 1973. Visual interference in the parafoveal recognition of initial and final letters of words. Vision Research 13, 4, 767--782.

[2]

Colas, F., Flacher, F., Tanner, T., Bessiere, P., and Girard, B. 2009. Bayesian models of eye movement selection with retinotopic maps. Biological Cybernetics 100, 3, 203--14.

Digital Library

[3]

Dechterenko, F., and Lukavsky, J. 2014. Models of eye movements in multiple object tracking with many objects. In 5th European Workshop on Visual Information Processing (EUVIP 2014), IEEE, 1--6.

[4]

Fehd, H. M., and Seiffert, A. E. 2008. Eye movements during multiple object tracking: where do participants look? Cognition 108, 1, 201--9.

[5]

Fehd, H. M. 2009. Eye movement strategies during attentional tracking. PhD thesis, Vanderbilt University.

[6]

Haykin, S. 1999. Neural Networks: A Comprehensive Foundation, 2nd ed. ed., vol. 13. Prentice Hall, Upper Saddle River.

Digital Library

[7]

Itti, L., Koch, C., and Niebur, E. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 11, 1254--1259.

Digital Library

[8]

Levi, D. M. 2008. CrowdingAn essential bottleneck for object recognition: A mini-review. Vision Research 48, 5, 635--654.

[9]

Lukavsky, J. 2013. Eye movements in repeated multiple object tracking. Journal of Vision 13, 7 (jun), 9--9.

[10]

Marquardt, D. W. 1963. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of the Society for Industrial and Applied Mathematics 11, 2 (jun), 431--441.

[11]

Posner, M. I., Snyder, C. R., and Davidson, B. J. 1980. Attention and the detection of signals. Journal of Experimental Psychology 109, 2, 160--174.

[12]

Pylyshyn, Z. W., and Storm, R. W. 1988. Tracking multiple independent targets: Evidence for a parallel tracking mechanism. Spatial Vision 3, 3, 179--197.

[13]

Scimeca, J. M., and Franconeri, S. L. 2015. Selecting and tracking multiple objects. Wiley Interdisciplinary Reviews: Cognitive Science 6, 2, 109--118.

[14]

Tatler, B. W., and Vincent, B. T. 2009. The prominence of behavioural biases in eye guidance. Visual Cognition 17, 6-7, 1029--1054.

[15]

Yarbus, A. L. 1967. Eye movements and vision. Plenum Press.

[16]

Zelinsky, G. J., and Neider, M. B. 2008. An eye movement analysis of multiple object tracking in a realistic environment. Visual Cognition 16, 5, 553--566.

Cited By

Krassanakis VPerreira Da Silva MRicordel V(2018)Monitoring Human Visual Behavior during the Observation of Unmanned Aerial Vehicles (UAVs) VideosDrones10.3390/drones20400362:4(36)Online publication date: 19-Oct-2018
https://doi.org/10.3390/drones2040036
Hild JVoit MKühnle CBeyerer JSharif BKrejtz K(2018)Predicting observer's task from eye movement patterns during motion image analysisProceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications10.1145/3204493.3204575(1-5)Online publication date: 14-Jun-2018
https://dl.acm.org/doi/10.1145/3204493.3204575

Index Terms

Predicting eye movements in multiple object tracking using neural networks
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology

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Published In

cover image ACM Conferences

ETRA '16: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications

March 2016

378 pages

ISBN:9781450341257

DOI:10.1145/2857491

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

Copyright © 2016 ACM.

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Published: 14 March 2016

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ETRA '16: 2016 Symposium on Eye Tracking Research and Applications

March 14 - 17, 2016

South Carolina, Charleston

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Cited By

Krassanakis VPerreira Da Silva MRicordel V(2018)Monitoring Human Visual Behavior during the Observation of Unmanned Aerial Vehicles (UAVs) VideosDrones10.3390/drones20400362:4(36)Online publication date: 19-Oct-2018
https://doi.org/10.3390/drones2040036
Hild JVoit MKühnle CBeyerer JSharif BKrejtz K(2018)Predicting observer's task from eye movement patterns during motion image analysisProceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications10.1145/3204493.3204575(1-5)Online publication date: 14-Jun-2018
https://dl.acm.org/doi/10.1145/3204493.3204575

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