Compression and recognition of dance gestures using a deformable model
In this paper, we aim for the recognition of a set of dance gestures from contemporary ballet. Our input data are motion trajectories followed by the joints of a dancing body provided by a motion-capture system. It is obvious that direct use of the original signals is unreliable and expensive. Therefore, we propose a suitable tool for non-uniform sub-sampling of spatio-temporal signals. The key to our approach is the use of a deformable model to provide a compact and efficient representation of motion trajectories. Our dance gesture recognition method involves a set of hidden Markov models (HMMs), each of them being related to a motion trajectory followed by the joints. The recognition of such movements is then achieved by matching the resulting gesture models with the input data via HMMs. We have validated our recognition system on 12 fundamental movements from contemporary ballet performed by four dancers.
deformable model gesture recognition hidden markov model contemporary dance data compression motion capture