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The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition
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, 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.
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Cited by
Collecting complex activity datasets in highly rich networked sensor environments
Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., et al. (2010) · 2010 Seventh International Conference on Networked Sensing Systems (INSS) · doi:10.1109/inss.2010.5573462 · Cited by 788
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S. T., Tröster, G., Millán, J. D. R., & Roggen, D. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.12.014 · Cited by 742
A database for fine grained activity detection of cooking activities
Rohrbach, M., Amin, S., Andriluka, M., & Schiele, B. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247801 · Cited by 521
Berkeley MHAD: A comprehensive Multimodal Human Action Database
Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., & Bajcsy, R. (2013) · 2013 IEEE Workshop on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2013.6474999 · Cited by 484
A survey of video datasets for human action and activity recognition
Chaquet, J. M., Carmona, E. J., & Fernández-Caballero, A. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.01.013 · Cited by 455
Metadata
- Authors
- Tenorth, M., Bandouch, J., & Beetz, M.
- Venue
- 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 1089-1096
- OpenAlex
- W2078046413
- Cited by
- 214
- References
- 0
- FWCI
- 11.18
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tenorth, M., Bandouch, J., & Beetz, M. (2009). The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. https://doi.org/10.1109/iccvw.2009.5457583
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