Technical University of Munich

@inproceedings{tenorth_tum_2009,
 author = {M Tenorth and J Bandouch and M Beetz},
 title = {The {TUM} Kitchen Data Set of Everyday Manipulation Activities for
	Motion Tracking and Action Recognition},
 booktitle = {{IEEE} International Workshop on Tracking Humans for the Evaluation
	of their Motion in Image Sequences ({THEMIS)}, in conjunction with
	{ICCV2009}},
 year = {2009},
 abstract = {We introduce the publicly available {TUM} Kitchen Data Set as a comprehensive
	collection of activity sequences recorded in a kitchen environment
	equipped with multiple complementary sensors. The recorded data consists
	of observations of naturally performed manipulation tasks as encountered
	in everyday activities of human life. Several instances of a table-setting
	task were performed by different subjects, involving the manipulation
	of objects and the environment. We provide the original video sequences,
	fullbody motion capture data recorded by a markerless motion tracker,
	{RFID} tag readings and magnetic sensor readings from objects and
	the environment, as well as corresponding action labels. In this
	paper, we both describe how the data was computed, in particular
	the motion tracker and the labeling, and give examples what it can
	be used for. We present first results of an automatic method for
	segmenting the observed motions into semantic classes, and describe
	how the data can be integrated in a knowledge-based framework for
	reasoning about the observations.},
}