skip to main content
10.1145/3204949.3208134acmconferencesArticle/Chapter ViewAbstractPublication PagesmmsysConference Proceedingsconference-collections
research-article

AVtrack360: an open dataset and software recording people's head rotations watching 360° videos on an HMD

Published:12 June 2018Publication History

ABSTRACT

In this paper, we present a viewing test with 48 subjects watching 20 different entertaining omnidirectional videos on an HTC Vive Head Mounted Display (HMD) in a task-free scenario. While the subjects were watching the contents, we recorded their head movements. The obtained dataset is publicly available in addition to the links and timestamps of the source contents used. Within this study, subjects were also asked to fill in the Simulator Sickness Questionnaire (SSQ) after every viewing session. Within this paper, at first SSQ results are presented. Several methods for evaluating head rotation data are presented and discussed. In the course of the study, the collected dataset is published along with the scripts for evaluating the head rotation data. The paper presents the general angular ranges of the subjects' exploration behavior as well as an analysis of the areas where most of the time was spent. The collected information can be presented as head-saliency maps, too. In case of videos, head-saliency data can be used for training saliency models, as information for evaluating decisions during content creation, or as part of streaming solutions for region-of-interest-specific coding as with the latest tile-based streaming solutions, as discussed also in standardization bodies such as MPEG.

References

  1. Shahryar Afzal, Jiasi Chen, and KK Ramakrishnan. 2017. Characterization of 360-degree Videos. In Proceedings of the Workshop on Virtual Reality and Augmented Reality Network. ACM, 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. ARTE G.E.I.E. 2018. ARTE360 VR. (2018). http://arte.tv/arte360Google ScholarGoogle Scholar
  3. Xavier Corbillon, Francesca De Simone, and Gwendal Simon. 2017. 360-degree video head movement dataset. In Proceedings of the 8th ACM on Multimedia Systems Conference. ACM, 199--204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Philip Day. 2018. Whirligig Free - Whirligig. (2018). http://www.whirligig.xyz/new-page-3Google ScholarGoogle Scholar
  5. Ana De Abreu, Cagri Ozcinar, and Aljosa Smolic. 2017. Look around you: Saliency maps for omnidirectional images in vr applications. In Quality of Multimedia Experience (QoMEX), 2017 Ninth International Conference on. IEEE, 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  6. Ching-Ling Fan, Jean Lee, Wen-Chih Lo, Chun-Ying Huang, Kuan-Ta Chen, and Cheng-Hsin Hsu. 2017. Fixation Prediction for 360 Video Streaming in Head-Mounted Virtual Reality. In Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM, 67--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. VR Industry Forum. 2017. LEXICON. Technical Report Version 1.0 2017-07-01. http://www.vr-if.org/lexicon.Google ScholarGoogle Scholar
  8. International Telecommunication Union. 2008. ITU-T P. 910. Subjective video quality assessment methods for multimedia applications (2008).Google ScholarGoogle Scholar
  9. Robert S Kennedy, Norman E Lane, Kevin S Berbaum, and Michael G Lilienthal. 1993. Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness. The international journal of aviation psychology 3, 3 (1993), 203--220.Google ScholarGoogle Scholar
  10. Wen-Chih Lo, Ching-Ling Fan, Jean Lee, Chun-Ying Huang, Kuan-Ta Chen, and Cheng-Hsin Hsu. 2017. 360 Video Viewing Dataset in Head-Mounted Virtual Reality. In Proceedings of the 8th ACM on Multimedia Systems Conference. ACM, 211--216. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Søren Merser, Bill Miller, and Ken Weiner. 2018. Latin Squares for Constructing "Williams Designs", Balanced for First-order Carry-over (Residual) Effects. (2018). http://statpages.info/latinsq.htmlGoogle ScholarGoogle Scholar
  12. Amy Pavel, Björn Hartmann, and Maneesh Agrawala. 2017. Shot Orientation Controls for Interactive Cinematography with 360 Video. In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology. ACM, 289--297. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Yashas Rai, Jesús Gutiérrez, and Patrick Le Callet. 2017. A dataset of head and eye movements for 360 degree images. In Proceedings of the 8th ACM on Multimedia Systems Conference. ACM, 205--210. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Taehyun Rhee, Lohit Petikam, Benjamin Allen, and Andrew Chalmers. 2017. Mr360: Mixed reality rendering for 360 panoramic videos. IEEE transactions on visualization and computer graphics 23, 4 (2017), 1379--1388. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ashutosh Singla, Stephan Fremerey, Werner Robitza, Pierre Lebreton, and Alexander Raake. 2017. Comparison of Subjective Quality Evaluation for HEVC Encoded Omnidirectional Videos at Different Bit-rates for UHD and FHD Resolution. In Proceedings of the on Thematic Workshops of ACM Multimedia 2017. ACM, 511--519. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Ashutosh Singla, Stephan Fremerey, Werner Robitza, and Alexander Raake. 2017. Measuring and comparing QoE and simulator sickness of omnidirectional videos in different head mounted displays. In Quality of Multimedia Experience (QoMEX), 2017 Ninth International Conference on. IEEE, 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  17. Vincent Sitzmann, Ana Serrano, Amy Pavel, Maneesh Agrawala, Diego Gutierrez, and Gordon Wetzstein. 2016. Saliency in VR: How do people explore virtual environments? arXiv preprint arXiv.1612.04335 (2016).Google ScholarGoogle Scholar
  18. Chenglei Wu, Zhihao Tan, Zhi Wang, and Shiqiang Yang. 2017. A Dataset for Exploring User Behaviors in VR Spherical Video Streaming. In Proceedings of the 8th ACM on Multimedia Systems Conference. ACM, 193--198. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Mai Xu, Yuhang Song, Jianyi Wang, Minglang Qiao, Liangyu Huo, and Zulin Wang. 2017. Modeling Attention in Panoramic Video: A Deep Reinforcement Learning Approach. arXiv preprint arXiv.1710.10755 (2017).Google ScholarGoogle Scholar

Index Terms

  1. AVtrack360: an open dataset and software recording people's head rotations watching 360° videos on an HMD

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference
          June 2018
          604 pages
          ISBN:9781450351928
          DOI:10.1145/3204949
          • General Chair:
          • Pablo Cesar,
          • Program Chairs:
          • Michael Zink,
          • Niall Murray

          Copyright © 2018 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 12 June 2018

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate176of530submissions,33%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader