On the improvement of anthropometry and pose estimation from a single uncalibrated image

Carlos Barrón, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Recently, we developed a technique that allows semi-automatic estimation of anthropometry and pose from a single image. However, estimation was limited to a class of images for which an adequate number of human body segments were almost parallel to the image plane. In this paper, we present a generalization of that estimation algorithm that exploits pairwise geometric relationships of body segments to allow estimation from a broader class of images. In addition, we refine our search space by constructing a fully populated discrete hyper-ellipsoid of stick human body models in order to capture the variance of the statistical anthropometric information. As a result, a better initial estimate can be computed by our algorithm and thus the number of iterations needed during minimization are reduced tenfold. We present our results over a variety of images to demonstrate the broad coverage of our algorithm.

Original languageEnglish (US)
Pages (from-to)229-236
Number of pages8
JournalMachine Vision and Applications
Issue number4
StatePublished - Sep 2003


  • Anthropometry
  • Articulated objects
  • Human motion estimation
  • Pose estimation
  • Tracking

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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