Real-Time Body Motion Analysis for Dance Pattern Recognition
Abstract
This paper presents an algorithm for real-time body motion analysis for dance pattern recognition by use of a dynamic stereo vision sensor. Dynamic stereo vision sensors asynchronously generate events upon scene dynamics, so that motion activities are on-chip segmented by the sensor. Using this sensor body motion analysis and tracking can be efficiently performed. For dance pattern recognition we use a machine learning method based on the Hidden Markov Model. Emphasis is laid on the analysis of the suitability for use in embedded systems. For testing the algorithm we use a dance choreography consisting of eight different activities and a training set of 430 recorded activities performed by 15 different persons. A cross validation on the data reached an average recognition rate of 94%.
Cite
Text
Kohn et al. "Real-Time Body Motion Analysis for Dance Pattern Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012. doi:10.1109/CVPRW.2012.6238894Markdown
[Kohn et al. "Real-Time Body Motion Analysis for Dance Pattern Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012.](https://mlanthology.org/cvprw/2012/kohn2012cvprw-realtime-a/) doi:10.1109/CVPRW.2012.6238894BibTeX
@inproceedings{kohn2012cvprw-realtime-a,
title = {{Real-Time Body Motion Analysis for Dance Pattern Recognition}},
author = {Kohn, Bernhard and Nowakowska, Aneta and Belbachir, Ahmed Nabil},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2012},
pages = {48-53},
doi = {10.1109/CVPRW.2012.6238894},
url = {https://mlanthology.org/cvprw/2012/kohn2012cvprw-realtime-a/}
}