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Constrained Optimization for Human Pose Estimation from Depth Sequences

  • Youding Zhu
  • Kikuo Fujimura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

A new 2-step method is presented for human upper-body pose estimation from depth sequences, in which coarse human part labeling takes place first, followed by more precise joint position estimation as the second phase. In the first step, a number of constraints are extracted from notable image features such as the head and torso. The problem of pose estimation is cast as that of label assignment with these constraints. Major parts of the human upper body are labeled by this process. The second step estimates joint positions optimally based on kinematic constraints using dense correspondences between depth profile and human model parts. The proposed framework is shown to overcome some issues of existing approaches for human pose tracking using similar types of data streams. Performance comparison with motion capture data is presented to demonstrate the accuracy of our approach.

Keywords

Motion Capture Depth Sequence Depth Image Inverse Kinematic Joint Position 
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 2007

Authors and Affiliations

  • Youding Zhu
    • 1
  • Kikuo Fujimura
    • 2
  1. 1.Computer Science and Engineering, The Ohio State University 
  2. 2.Honda Research InstituteUSA

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