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.

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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

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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