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Data2MV - A user behaviour dataset for multi-view scenarios.

Tiago Soares da Costa1, Maria Teresa Andrade2,1, Paula Viana3,1

  • 1Centre for Telecommunications and Multimedia, INESC TEC, Porto, Portugal.

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|November 29, 2023
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Summary

The Data2MV dataset offers valuable gaze fixation data from 45 participants viewing videos, aiding research in immersive streaming. This dataset includes over a million gaze fixations and associated saliency maps.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Multimedia Systems

Background:

  • Gaze tracking data is crucial for understanding user behavior in multimedia environments.
  • A scarcity of comprehensive datasets hinders research in immersive, multi-view streaming scenarios.
  • Existing datasets often lack the scale and detail required for advanced analysis.

Purpose of the Study:

  • To introduce and release the Data2MV dataset, a novel collection of gaze fixation data.
  • To provide researchers with a rich resource for studying visual attention in video consumption.
  • To support the development of new algorithms for saliency prediction and user behavior analysis.

Main Methods:

  • Collected gaze fixation data from 45 participants using an Intel RealSense F200 camera.
  • Utilized seven 20-minute video playlists, with data recorded at a 0.05-second interval.
  • Generated saliency maps from aggregate fixation data and provided open-source software tools.

Main Results:

  • The dataset comprises 1,000,845 gaze fixations across 128 experiments.
  • Includes 68,393 image frames and an equal number of aggregate saliency maps.
  • The Data2MV dataset is publicly available on Mendeley Data.

Conclusions:

  • The Data2MV dataset addresses the scarcity of gaze fixation data in immersive streaming.
  • It serves as a valuable resource for advancing research in visual attention and HCI.
  • Facilitates the development and validation of computational models for saliency and user behavior.