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An empirical pipeline to derive gaze prediction heuristics for 3D action games

An empirical pipeline to derive gaze prediction heuristics for 3D action games An Empirical Pipeline to Derive Gaze Prediction Heuristics for 3D Action Games MATTHIAS BERNHARD, Technical University of Vienna EFSTATHIOS STAVRAKIS, INRIA/REVES MICHAEL WIMMER, Technical University of Vienna 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 de ned 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 rst-person shooter game. In particular, a protocol is presented describing the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Applied Perception (TAP) Association for Computing Machinery

An empirical pipeline to derive gaze prediction heuristics for 3D action games

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2010 by ACM Inc.
ISSN
1544-3558
DOI
10.1145/1857893.1857897
Publisher site
See Article on Publisher Site

Abstract

An Empirical Pipeline to Derive Gaze Prediction Heuristics for 3D Action Games MATTHIAS BERNHARD, Technical University of Vienna EFSTATHIOS STAVRAKIS, INRIA/REVES MICHAEL WIMMER, Technical University of Vienna 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 de ned 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 rst-person shooter game. In particular, a protocol is presented describing the

Journal

ACM Transactions on Applied Perception (TAP)Association for Computing Machinery

Published: Oct 1, 2010

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