The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition


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, full-body 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.

Extracted Key Phrases

11 Figures and Tables

Citations per Year

200 Citations

Semantic Scholar estimates that this publication has 200 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Tenorth2009TheTK, title={The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition}, author={Moritz Tenorth and Jan Bandouch and Michael Beetz}, journal={2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops}, year={2009}, pages={1089-1096} }