Real-time body motion analysis for dance pattern recognition
Bernhard Kohn, Aneta Nowakowska, Ahmed Nabil Belbachir · 2012
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%.