Literature DB >> 20160298

DynAOI: a tool for matching eye-movement data with dynamic areas of interest in animations and movies.

Frank Papenmeier1, Markus Huff.   

Abstract

Analyzing gaze behavior with dynamic stimulus material is of growing importance in experimental psychology; however, there is still a lack of efficient analysis tools that are able to handle dynamically changing areas of interest. In this article, we present DynAOI, an open-source tool that allows for the definition of dynamic areas of interest. It works automatically with animations that are based on virtual three-dimensional models. When one is working with videos of real-world scenes, a three-dimensional model of the relevant content needs to be created first. The recorded eye-movement data are matched with the static and dynamic objects in the model underlying the video content, thus creating static and dynamic areas of interest. A validation study asking participants to track particular objects demonstrated that DynAOI is an efficient tool for handling dynamic areas of interest.

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Year:  2010        PMID: 20160298     DOI: 10.3758/BRM.42.1.179

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  5 in total

1.  Gaze entropy reflects surgical task load.

Authors:  Leandro L Di Stasi; Carolina Diaz-Piedra; Héctor Rieiro; José M Sánchez Carrión; Mercedes Martin Berrido; Gonzalo Olivares; Andrés Catena
Journal:  Surg Endosc       Date:  2016-03-16       Impact factor: 4.584

2.  A toolkit for wide-screen dynamic area of interest measurements using the Pupil Labs Core Eye Tracker.

Authors:  Yasmin Faraji; Joris W van Rijn; Ruth M A van Nispen; Ger H M B van Rens; Bart J M Melis-Dankers; Jan Koopman; Laurentius J van Rijn
Journal:  Behav Res Methods       Date:  2022-10-17

Review 3.  Eye Behavior During Multiple Object Tracking and Multiple Identity Tracking.

Authors:  Jukka Hyönä; Jie Li; Lauri Oksama
Journal:  Vision (Basel)       Date:  2019-07-31

4.  A skeleton-based approach to analyzing oculomotor behavior when viewing animated characters.

Authors:  Thibaut Le Naour; Jean-Pierre Bresciani
Journal:  J Eye Mov Res       Date:  2017-12-18       Impact factor: 0.957

5.  Using Eye Movement Data Visualization to Enhance Training of Air Traffic Controllers: A Dynamic Network Approach.

Authors:  Saptarshi Mandal; Ziho Kang
Journal:  J Eye Mov Res       Date:  2018-08-08       Impact factor: 0.957

  5 in total

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