Electronics and Telecommunications Trends (전자통신동향분석)

Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Engagement Analysis Technology for Tele-presence Services

텔레프레즌스 서비스를 위한 몰입도 분석 기술

  • Published : 2017.10.01
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Abstract

A Telepresence service is an advanced video conferencing service at aimed providing remote users with the feeling of being present together at a particular location for a face-to-face group meeting. The effectiveness in this type of meeting can be further increased by automatically recognizing the audiovisual behaviors of the video conferencing users, accurately inferring their level of engagement from the recognized reactions, and providing proper feedback on their engagement state. In this paper, we review the recent developments of such engagement analysis techniques being utilized in various applications, such as human-robot interaction, content evaluation, telematics, and online collaboration services. In addition, we introduce a real-time engagement analysis framework employed in our telepresence service platform for an increased participation in online group collaboration settings.

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Acknowledgement

Grant : Giga Media 기반 Tele-Experience 서비스 SW플랫폼 기술 개발

Supported by : 과학기술정보통신부

References

  1. 이미숙 외, "Tele-experience 실감 스마트워크 서비스를 위한 텔레프레즌스 기술," 한국통신학회지: 정보와 통신, 제31권 제3호, 2014, pp. 3-11.
  2. 정주현 외, "시각적 반응을 이용한 사용자 몰입 경험 (UX) 평가," 대한인간공학회 학술대회논문집, 2012, pp. 347-351.
  3. Y. LI et al., "Towards Measuring and Inferring User Interest from Gaze," In Proc. Int. Conf. World Wide Web Companion, Perth, Austrailia, Apr. 3-7, 2017, pp. 525-533.
  4. M. Sun, Z. Zhao, and X. Ma, "Sensing and Handling Engagement Dynamics in Human-Robot Interaction Involving Peripheral Computing Devices," In Proc. Conf. Human Factors Comput. Syst., Denver, CO, USA, May 6-11, 2017, pp. 556-567.
  5. M. Kutila et al., "Driver Distraction Detection with a Camera Vision System," In Proc. IEEE Int. Conf. Image Process., San Antonio, TX, USA, Sept. 16-19, 2007, pp. VI-201-VI-204.
  6. Y. Abouelnaga et al., "Real-Time Distracted Driver Posture Classification," arXiv preprint, arXiv:1706.09498, 2017.
  7. M. Frank et al., "Engagement Detection in Meetings," arXiv preprint, arXiv:1608.08711, 2016.
  8. G. Tofighi, H. Gu, and K. Raahemifar, "Vision-Based Engagement Detection in Virtual Reality," Digital Media Ind. Academic Forum, Santorini, Greece, July 4-6, 2016, pp. 202-206.