Design and implementation of a head-pose estimation system used with large-scale screens

Sang Heon Lee, Myoung Kyu Sohn, Dong Ju Kim, Hyunduk Kim, Nuri Ryu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, we propose a novel head-pose estimation system for use with a large-scale screen to provide intelligent interaction with content. The head image of the user is captured from a RGB-D (red, green, blue pixel value, and depth data) camera connected to a large-scale display system. The head orientation of the user is then estimated from the RGB-D data by using the random regression forest algorithm. The random regression forest algorithm is a very powerful tool for generalization problems that does not suffer from overfitting. By using the head-pose estimation system, the user's region-of-interest (ROI) is found in a large-scale screen. After the ROI is found, various intelligent interactions with content can be possible. As future work, a hand gesture recognition system will be jointly connected with this head-pose estimation system in order to control the user's gestures more precisely in the ROI.

Original languageEnglish
Title of host publication2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
Pages282-283
Number of pages2
DOIs
StatePublished - 2013
Event2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013 - Tokyo, Japan
Duration: 1 Oct 20134 Oct 2013

Publication series

Name2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013

Conference

Conference2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
Country/TerritoryJapan
CityTokyo
Period1/10/134/10/13

Keywords

  • Hand gesture recognition
  • Head-pose estimation
  • Random regression forest
  • intelligent interaction

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