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Full Body Tracking from Multiple Views Using Stochastic Sampling

R. Kehl, M. Bray and L. Van Gool
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
San Diego, CA, June 2005

Abstract

We present a novel approach for full body pose tracking using stochastic sampling. A volumetric reconstruction of a person is extracted from silhouettes in multiple video images. Then, an articulated body model is fitted to the data with stochastic meta descent (SMD) optimization. By comparing even a simplified version of SMD to the commonly used Levenberg-Marquardt method, we demonstrate the power of stochastic compared to deterministic sampling, especially in cases of noisy and incomplete data. Moreover, color information is added to improve the speed and robustness of the tracking. Results are shown for several challenging sequences, with tracking of 24 degrees of freedom in less than 1 second per frame.


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@InProceedings{eth_biwi_00359,
  author = {R. Kehl and M. Bray and L. Van Gool},
  title = {Full Body Tracking from Multiple Views Using Stochastic Sampling},
  booktitle = {2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  year = {2005},
  month = {June},
  pages = {129-136},
  keywords = {Full Body Tracking, SMD, Stochastic Sampling, Volumetric Reconstruction, Linear Skin Blending}
}