Deformable model based data compression for gesture recognition

We aim at recognizing 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 nonuniform, sub-sampling of spatio-temporal signals. The key of our approach is the use of a deformable model to provide a compact and efficient representation of motion trajectories.

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