Volume XLII-2/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 587-593, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W3-587-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 587-593, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W3-587-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Feb 2017

23 Feb 2017

FOLK DANCE PATTERN RECOGNITION OVER DEPTH IMAGES ACQUIRED VIA KINECT SENSOR

E. Protopapadakis1, A. Grammatikopoulou2, A. Doulamis1, and N. Grammalidis2 E. Protopapadakis et al.
  • 1School of Rural and Surveying Engineering, National Technical University of Athens, 9 Iroon Polytechneiou str, 15780 Zografou, Greece
  • 2Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

Keywords: Intangible Cultural Heritage, Folk Dances, Depth Camera, Unsupervised Clustering, Skeleton data

Abstract. The possibility of accurate recognition of folk dance patterns is investigated in this paper. System inputs are raw skeleton data, provided by a low cost sensor. In particular, data were obtained by monitoring three professional dancers, using a Kinect II sensor. A set of six traditional Greek dances (without their variations) consists the investigated data. A two-step process was adopted. At first, the most descriptive skeleton data were selected using a combination of density based and sparse modelling algorithms. Then, the representative data served as training set for a variety of classifiers.