Traditional African Dances Preservation Using Deep Learning Techniques

Abstract

Human action recognition continues to evolve and improve through deep learning techniques. There have been studies with some success in the field of action recognition, but only a few of them have focused on traditional dance. This is because dance actions, especially in traditional African dance, are long and involve fast movements. 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 dances are an important part of African culture and heritage. Digital preservation of these dances in their multitude and form is a challenging problem. The dance dataset was constituted from freely available YouTube videos. Four traditional African dances were used for the dance classification process: Adowa, Swange, Bata, and Sinte dance. Five Convolutional Neural Network (CNN) models were used for the classification and achieved an accuracy between 93% and 98%. Additionally, human pose estimation algorithms were applied to Sinte dance. A model of Sinte dance that can be exported to other environments was obtained.