@InProceedings{tenorth09dataset,
  author =       {Moritz Tenorth and Jan Bandouch and Michael 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},
  url = {https://ai.uni-bremen.de/papers/tenorth09dataset.pdf},
  bib2html_pubtype ={Conference Paper},
  bib2html_rescat  = {Perception},
  bib2html_groups = {Memoman, K4C},
  bib2html_funding = {CoTeSys},
  bib2html_domain  = {Assistive Household},
  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.},
  keywords = {openease_human_activity, openease_kbe_humans_table},
}