Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation (bibtex)
by Jan Bandouch, Florian Engstler, Michael Beetz
Abstract:
A common approach to the problem of 3D human pose estimation from video is to recursively estimate the most likely pose via particle filtering. However, standard particle filtering methods fail the task due to the high dimensionality of the 3D articulated human pose space. In this paper we present a thorough evaluation of two variants of particle filtering, namely Annealed Particle Filtering and Partitioned Sampling Particle Filtering, that have been proposed to make the problem feasible by exploiting the hierarchical structures inside the pose space. We evaluate both methods in the context of markerless model-based 3D motion capture using silhouette shapes from multiple cameras. For that we created a simulation from ground truth sequences of human motions, which enables us to focus our evaluation on the sampling capabilities of the approaches, i.e. on how efficient particles are spread towards the modes of the distribution. We show the behaviour with respect to the amount of cameras used, the amount of particles used, as well as the dimensionality of the search space. Especially the performance when using more complex human models (40 DOF and above) that are able to capture human movements with higher precision compared to previous approaches is of interest in this work. In summary, we show that both methods have complementary strengths, and propose a combined method that is able to perform the tracking task with higher robustness despite reduced computational effort.
Reference:
Jan Bandouch, Florian Engstler, Michael Beetz, "Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation", In Proceedings of the 19th British Machine Vision Conference (BMVC), 2008.
Bibtex Entry:
@InProceedings{bandouch08bmvc,
  author = {Jan Bandouch and Florian Engstler and Michael Beetz},
  title = {{Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation}},
  year =	 {2008},
  booktitle =	 {Proceedings of the 19th British Machine Vision Conference (BMVC)},
  bib2html_pubtype ={Conference Paper},
  bib2html_rescat  = {Perception},
  bib2html_groups ={Memoman},
  bib2html_funding = {CoTeSys},
  bib2html_domain  = {Assistive Household},

  abstract =     {A common approach to the problem of 3D human pose estimation from video
                  is to recursively estimate the most likely pose via particle filtering.
                  However, standard particle filtering methods fail the task due to the high
                  dimensionality of the 3D articulated human pose space.
                  In this paper we present a thorough evaluation of two variants of particle
                  filtering, namely Annealed Particle Filtering and Partitioned Sampling
                  Particle Filtering, that have been proposed to make the problem feasible by
                  exploiting the hierarchical structures inside the pose space. We evaluate
                  both methods in the context of markerless model-based 3D motion capture
                  using silhouette shapes from multiple cameras. For that we created a
                  simulation from ground truth sequences of human motions, which enables
                  us to focus our evaluation on the sampling capabilities of the approaches,
                  i.e. on how efficient particles are spread towards the modes of the
                  distribution. We show the behaviour with respect to the amount of cameras
                  used, the amount of particles used, as well as the dimensionality of the
                  search space. Especially the performance when using more complex human
                  models (40 DOF and above) that are able to capture human movements
                  with higher precision compared to previous approaches is of interest
                  in this work.
                  In summary, we show that both methods have complementary strengths, and
                  propose a combined method that is able to perform the tracking task with
                  higher robustness despite reduced computational effort.}
}
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