<p>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.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14501736 |
| Date | 06 May 2021 |
| Creators | Adebunmi Elizabeth Odefunso (10711203) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Identification_classification_and_modelling_of_Traditional_African_dances_using_deep_learning_techniques/14501736 |
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