An Empirical Pipeline to Derive Gaze Prediction Heuristics for 3D Action Games

Matthias Bernhard, Efstathios Stavrakis, Michael Wimmer
An Empirical Pipeline to Derive Gaze Prediction Heuristics for 3D Action Games
ACM Transactions on Applied Perception, 8(1):4:1-4:30, October 2010.

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Abstract

Gaze analysis and prediction in interactive virtual environments, such as games, is a challenging topic since the 3D perspective and variations of the viewpoint as well as the current task introduce many variables that affect the distribution of gaze. In this article, we present a novel pipeline to study eye-tracking data acquired from interactive 3D applications. The result of the pipeline is an importance map which scores the amount of gaze spent on each object. This importance map is then used as a heuristic to predict a user’s visual attention according to the object properties present at runtime. The novelty of this approach is that the analysis is performed in object space and the importance map is defined in the feature space of high-level properties. High-level properties are used to encode task relevance and other attributes, such as eccentricity, which may have an impact on gaze behavior.

The pipeline has been tested with an exemplary study on a first-person shooter game. In particular, a protocol is presented describing the data acquisition procedure, the learning of different importance maps from the data, and finally an evaluation of the performance of the derived gaze predictors. A metric measuring the degree of correlation between attention predicted by the importance map and the actual gaze yielded clearly positive results. The correlation becomes particularly strong when the player is attentive to an in-game task.

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BibTeX

@article{bernhard-2010-gph,
  title =      "An Empirical Pipeline to Derive Gaze Prediction Heuristics
               for 3D Action Games",
  author =     "Matthias Bernhard and Efstathios Stavrakis and Michael
               Wimmer",
  year =       "2010",
  abstract =   "Gaze analysis and prediction in interactive virtual
               environments, such as games, is a challenging topic since
               the 3D perspective and variations of the viewpoint as well
               as the current task introduce many variables that affect the
               distribution of gaze. In this article, we present a novel
               pipeline to study eye-tracking data acquired from
               interactive 3D applications. The result of the pipeline is
               an importance map which scores the amount of gaze spent on
               each object. This importance map is then used as a heuristic
               to predict a user’s visual attention according to the
               object properties present at runtime. The novelty of this
               approach is that the analysis is performed in object space
               and the importance map is defined in the feature space of
               high-level properties. High-level properties are used to
               encode task relevance and other attributes, such as
               eccentricity, which may have an impact on gaze behavior. 
               The pipeline has been tested with an exemplary study on a
               first-person shooter game. In particular, a protocol is
               presented describing the data acquisition procedure, the
               learning of different importance maps from the data, and
               finally an evaluation of the performance of the derived gaze
               predictors. A metric measuring the degree of correlation
               between attention predicted by the importance map and the
               actual gaze yielded clearly positive results. The
               correlation becomes particularly strong when the player is
               attentive to an in-game task.",
  month =      oct,
  issn =       "1544-3558",
  journal =    "ACM Transactions on Applied Perception",
  number =     "1",
  volume =     "8",
  pages =      "4:1--4:30",
  keywords =   "gaze predictor, video games, virtual environments,
               eye-tracking, gaze analysis",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2010/bernhard-2010-gph/",
}