Global Hand Pose Estimation by Multiple Camera Ellipse Tracking

  • Jorge Usabiaga
  • Ali Erol
  • George Bebis
  • Richard Boyle
  • Xander Twombly
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


Immersive virtual environments with life-like interaction capabilities have very demanding requirements including high precision and processing speed. These issues raise many challenges for computer vision-based motion estimation algorithms. In this study, we consider the problem of hand tracking using multiple cameras and estimating its 3D global pose (i.e., position and orientation of the palm). Our interest is in developing an accurate and robust algorithm to be employed in an immersive virtual training environment, called ”Virtual GloveboX” (VGX) [1], which is currently under development at NASA Ames. In this context, we present a marker-based, hand tracking and 3D global pose estimation algorithm that operates in a controlled, multi-camera, environment built to track the user’s hand inside VGX. The key idea of the proposed algorithm is tracking the 3D position and orientation of an elliptical marker placed on the dorsal part of the hand using model-based tracking approaches and active camera selection. It should be noted that, the use of markers is well justified in the context of our application since VGX naturally allows for the use of gloves without disrupting the fidelity of the interaction. Our experimental results and comparisons illustrate that the proposed approach is more accurate and robust than related approaches. A byproduct of our multi-camera ellipse tracking algorithm is that, with only minor modifications, the same algorithm can be used to automatically re-calibrate (i.e., fine-tune) the extrinsic parameters of a multi-camera system leading to more accurate pose estimates.


Multiple Camera Extrinsic Parameter Matching Error Reprojection Error Hand Tracking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jorge Usabiaga
    • 1
  • Ali Erol
    • 1
  • George Bebis
    • 1
  • Richard Boyle
    • 2
  • Xander Twombly
    • 2
  1. 1.Computer Vision LaboratoryUniversity of NevadaRenoUSA
  2. 2.BioVis LaboratoryNASA Ames Research CenterUSA

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