Ballet Pose Recognition: A Bag-of-Words Support Vector Machine Model for the Dance Training Environment

Serious dance students are always looking for ways in which they can improve their technique by practising alone at home or a studio by using a mirror for feedback. The problem these students face is that for many ballet postures it is difficult to analyze one’s own faults. By not having guidance regarding proper positional alignment, dancers risk developing injuries and bad habits. The proposed solution is a system which recognizes the ballet position being performed by a dancer. After recognition, this research aims to work towards providing the necessary correction as feedback. The results for recognition in the system, using a Bag-of-Words approach to a Support Vector Machine classifier, showed an accuracy of 59.6%. Multiple implementations are produced and assessed in this paper. It is clearly found that the approach is feasible, however, work for improving the accuracy is required. Recommendations to improve effective pose recognition for future work are therefore discussed.