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Identification, classification and modelling of Traditional African dances using deep learning techniques
Human action recognition continues to evolve and is examined better using deep learning techniques. Several successes have been recorded in the field of action recognition but only very few has focused on dance. This is because dance actions and, especially Traditional African dance, are long and involve fast movement of body parts. This research proposes a novel framework that applies data science algorithms to the field of cultural preservation by applying various deep learning techniques to identify, classify and model Traditional African dances from videos. Traditional African dances are important part of the African culture and heritage. Digital preservation of these dances in their myriad forms is a problem. The dance dataset was constituted using freely available YouTube videos. Three Traditional African dances – Adowa, Bata and Swange – were used for the dance classification process. Two Convolutional Neural Network (CNN) models were used for the classification and they achieved an accuracy of 97% and 98% respectively. Sound classification of Adowa, Bata and Swange drum ensembles were also carried out; an accuracy of 96% was achieved. Human Pose Estimation Algorithms were applied to the Sinte dance. A model of Sinte dance, which can be exported to other environments, was obtained.