Single-Frame 3D Human Pose Recovery from Multiple Views

  • Michael Hofmann
  • Dariu M. Gavrila
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5748)


We present a system for the estimation of unconstrained 3D human upper body pose from multi-camera single-frame views. Pose recovery starts with a shape detection stage where candidate poses are generated based on hierarchical exemplar matching in the individual camera views. The hierarchy used in this stage is created using a hybrid clustering approach in order to efficiently deal with the large number of represented poses. In the following multi-view verification stage, poses are re-projected to the other camera views and ranked according to a multi-view matching score. A subsequent gradient-based local pose optimization stage bridges the gap between the used discrete pose exemplars and the underlying continuous parameter space. We demonstrate that the proposed clustering approach greatly outperforms state-of-the-art bottom-up clustering in parameter space and present a detailed experimental evaluation of the complete system on a large data set.


Camera View World Coordinate System Correct Hypothesis Foreground Segmentation Shape Detection 
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 2009

Authors and Affiliations

  • Michael Hofmann
    • 1
  • Dariu M. Gavrila
    • 2
  1. 1.TNO Defence, Security and SafetyThe Netherlands
  2. 2.Intelligent Systems Laboratory, Faculty of ScienceUniversity of AmsterdamThe Netherlands

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