Technical University of Munich

@inproceedings{bandouch_tracking_2009,
 author = {J Bandouch and M Beetz},
 title = {Tracking Humans Interacting with the Environment Using Efficient
	Hierarchical Sampling and Layered Observation Models},
 booktitle = {{IEEE} Int. Workshop on Human-Computer Interaction ({HCI).} In conjunction
	with {ICCV2009}},
 year = {2009},
 abstract = {We present a markerless tracking system for unconstrained human motions
	which are typical for everyday manipulation tasks. Our system is
	capable of tracking a high-dimensional human model (51 {DOF)} without
	constricting the type of motion and the need for training sequences.
	The system reliably tracks humans that frequently interact with the
	environment, that manipulate objects, and that can be partially occluded
	by the environment. We describe and discuss two key components that
	substantially contribute to the accuracy and reliability of the system.
	First, a sophisticated hierarchical sampling strategy for recursive
	Bayesian estimation that combines partitioning with annealing strategies
	to enable efficient search in the presence of many local maxima.
	Second, a simple yet effective appearance model that allows for the
	combination of shape and appearance masks to implicitly deal with
	two cases of environmental occlusions by (1) subtracting dynamic
	non-human objects from the region of interest and (2) modeling objects
	(e.g. tables) that both occlude and can be occluded by human subjects.
	The appearance model is based on bit representations that makes our
	algorithm well suited for implementation on highly parallel hardware
	such as commodity {GPUs.} Extensive evaluations on the {HumanEva2}
	benchmarks show the potential of our method when compared to state-of-the-art
	Bayesian techniques. Besides the {HumanEva2} benchmarks, we present
	results on more challenging sequences, including table setting tasks
	in a kitchen environment and persons getting into and out of a car
	mock-up.},
}