by M Tenorth, J Bandouch and M Beetz
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.
Reference:
The TUM Kitchen Data Set of Everyday Manipulation Activities for Motion Tracking and Action Recognition (M Tenorth, J Bandouch and M Beetz), In IEEE International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences (THEMIS), in conjunction with ICCV2009, 2009.
Bibtex Entry:
@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.},
}