Gaze-To-Object Mapping During Visual Search in 3D Virtual Environments

Matthias Bernhard, Efstathios Stavrakis, Michael Hecher, Michael Wimmer
Gaze-To-Object Mapping During Visual Search in 3D Virtual Environments
ACM Transactions on Applied Perception (Special Issue SAP 2014), 11(3):14:1-14:17, August 2014. [draft] [video]

Information

Abstract

Stimuli obtained from highly dynamic 3D virtual environments and synchronous eye-tracking data are commonly used by algorithms that strive to correlate gaze to scene objects, a process referred to as Gaze-To-Object Mapping (GTOM). We propose to address this problem with a probabilistic approach using Bayesian inference. The desired result of the inference is a predicted probability density function (PDF) specifying for each object in the scene a probability to be attended by the user. To evaluate the quality of a predicted attention PDF, we present a methodology to assess the information value (i.e., likelihood) in the predictions of dierent approaches that can be used to infer object attention. To this end, we propose an experiment based on a visual search task which allows us to determine the object of attention at a certain point in time under controlled conditions. We perform this experiment with a wide range of static and dynamic visual scenes to obtain a ground-truth evaluation data set, allowing us to assess GTOM techniques in a set of 30 particularly challenging cases.

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BibTeX

@article{bernhard-2014-GTOM,
  title =      "Gaze-To-Object Mapping During Visual Search in 3D Virtual
               Environments ",
  author =     "Matthias Bernhard and Efstathios Stavrakis and Michael
               Hecher and Michael Wimmer",
  year =       "2014",
  abstract =   "Stimuli obtained from highly dynamic 3D virtual environments
               and synchronous eye-tracking data are commonly used by
               algorithms that strive to correlate gaze to scene objects, a
               process referred to as Gaze-To-Object Mapping (GTOM). We
               propose to address this problem with a probabilistic
               approach using Bayesian inference. The desired result of the
               inference is a predicted probability density function (PDF)
               specifying for each object in the scene a probability to be
               attended by the user. To evaluate the quality of a predicted
               attention PDF, we present a methodology to assess the
               information value (i.e., likelihood) in the predictions of
               dierent approaches that can be used to infer object
               attention. To this end, we propose an experiment based on a
               visual search task which allows us to determine the object
               of attention at a certain point in time under controlled
               conditions. We perform this experiment with a wide range of
               static and dynamic visual scenes to obtain a ground-truth
               evaluation data set, allowing us to assess GTOM techniques
               in a set of 30 particularly challenging cases.",
  month =      aug,
  issn =       "1544-3558",
  journal =    "ACM Transactions on Applied Perception (Special Issue SAP
               2014)",
  number =     "3",
  volume =     "11",
  pages =      "14:1--14:17",
  keywords =   "object-based attention, eye-tracking, virtual environments,
               visual attention",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2014/bernhard-2014-GTOM/",
}