Human Motion " Following " system using Hidden Markov Models and application to dance performance
Résumé
This work presents a movement “following” system based on “Hidden Markov Models” and motion descriptors extracted from video. The primary application is in performing arts such as dance but the methodology is general enough to be applied in other fields as long as appropriate descriptors are available.
Different motion features, segmentations, decoding methods and data analysis such as Principal Components analysis have been performed and compared in order to show how they affect the “following” performances. Both contemporary dance performances and synthetic animations have been used in order to evaluate the system.
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