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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Khalif Amir Zakry
Universiti Malaysia Sarawak
Computer Science & IT Engineering

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Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0 Khalif Amir Zakry; Irwandi Hipiny; Hamimah Ujir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp602-609

Abstract

Dancing is an art form of creative expression that is based on movement. Dancing comprises varying styles, pacing and composition to convey an artist’s expression. Thus, the classification of any dance to a certain genre or type depends on how accurate or similar it is to what is generally understood to be the specific movements of that dance type. This presents a problem for new dancers to assess if the dance movements that they have just learned is accurate or not to what the original dance type is. This paper proposed that deep learning models can classify dance videos of amateur dancers according to the similar movements of actions of several dance classes. For this study, AlexNet, ResNet and SqueezeNet models was used to perform training on multiple frames of actions of several dance videos for label prediction and the classification accuracy of the models during each training epoch is compared. This study observed that the average classification accuracy of the deep learning models is 94.9669% and is comparable to other approaches used for dance classifications.
Co-Authors Hamimah Ujir Irwandi Hipiny