Human pose estimation using two RGB-D sensors

Wanxin Xu, Po Chang Su, Sen Ching S. Cheung

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

4 Scopus citations


Accurate human pose estimation plays an important role in various applications such as sports analysis, health care and gaming. Even though recent approaches have shown that 3D positions of body joints can be estimated from a single depth sensor, the depth data often suffer from sensing noise and self-occlusion. In this paper, we present a novel pipeline to estimate the pose of a human body by using two depth sensors. The two sensors simultaneously capture the front and back of the body's movement. Using a wide-baseline RGB-D camera calibration algorithm, the two 3D scans are first geometrically aligned, and then registered to a generic human template using a Gaussian-mixture-model based point set registration procedure with local structure constraints. The new pose of person is finally estimated by a rigid bone-based pose transformation. Experimental results demonstrate the effectiveness of our system in estimating the body pose over other state-of-the-arts techniques.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Number of pages5
ISBN (Electronic)9781467399616
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2016 IEEE.


  • Human pose estimation
  • Non-rigid registration
  • RGB-D cameras

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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