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Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation (bibtex)
@INPROCEEDINGS{bandouch_evaluation_2008,
  author = {Bandouch, Jan and Engstler, Florian and Beetz, Michael},
  title = {Evaluation of Hierarchical Sampling Strategies in {3D} Human Pose
	Estimation},
  booktitle = {Proceedings of the 19th British Machine Vision Conference ({BMVC)}},
  year = {2008},
  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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Last edited 29.01.2013 17:37 by Quirin Lohr