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Scaled motion dynamics for markerless motion capture

B. Rosenhahn, Thomas Brox, H. Seidel
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2007
Abstract: This work proposes a way to use a-priori knowledge on motion dynamics for markerless human motion capture (MoCap). Specifically, we match tracked motion patterns to training patterns in order to predict states in successive frames. Thereby, modeling the motion by means of twists allows for a proper scaling of the prior. Consequently, there is no need for training data of different frame rates or velocities. Moreover, the method allows to combine very different motion patterns. Experiments in indoor and outdoor scenarios demonstrate the continuous tracking of familiar motion patterns in case of artificial frame drops or in situations insufficiently constrained by the image data.


Other associated files : rosenhahn_cvpr07.pdf [1.2MB]  

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BibTex reference

@InProceedings{Bro07h,
  author       = "B. Rosenhahn and T. Brox and H.-P. Seidel",
  title        = "Scaled motion dynamics for markerless motion capture",
  booktitle    = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)",
  month        = "Jun.",
  year         = "2007",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2007/Bro07h"
}

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