Ballroom dance step recognition by means of video processing and Artificial Intelligence techniques.
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http://hdl.handle.net/2183/32834
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Ballroom dance step recognition by means of video processing and Artificial Intelligence techniques.Autor(es)
Director(es)
Garabato, D.Vázquez, Carlos
Data
2022Centro/Dpto/Entidade
Universidade da Coruña. Facultade de InformáticaDescrición
Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/2023Resumo
[Abstract]: Ballroom dancing, or DanceSport is a sport of great complexity due to the number of dance
steps that make up every type of dance. This project tries to serve of help to dancers of all
levels, both to amateur and professional ones as well as to competition judges in order to
evaluate in a more objective manner. To do so, Artificial Intelligence techniques are applied
to classify the different dance steps.
There were filmed some Rumba choreographies by professional dancers and other videos
were collected from external sources. Features from the videos of the datasets are extracted
and selected, in terms of body keypoints and movement features for said keypoints to train the
models. In order to do so, it is necessary to label all the dance steps in each video, indicating
its start and ending times. Thereafter, models are trained with different Artificial Intelligence
algorithms, including Multi-Layer Perceptron and Long Short-Term Memory Recurrent Neural
Networks, and different feature inputs and parameters. Each model is tested against data
from different datasets, such as the custom or the reference dataset. Finally, performance
metrics are calculated for each model, and compared against the others. [Resumo]: Baile deportivo ou baile de salón é un deporte de gran complexidade debido ao número
de pasos que compoñen cada tipo de baile. Este proxecto trata de servir de axuda a bailaríns
de todos os niveis, tanto a principiantes como a profesionais e tamén a xuíces de competición
para avaliar de maneira máis obxectiva. Para iso, aplicáronse técnicas de Intelixencia Artificial
para clasificar os distintos pasos de baile.
Graváronse algunhas coreografías de Rumba bailadas por profesionais e outros vídeos foron
recopilados de recursos externos. Se extraen e seleccionan características dos vídeos dos
datasets en termos de keypoints do corpo e características de movemento para ditos keypoints
para entrenar os modelos. Para poder facer iso, é preciso etiquetar todos os pasos de baile en
cada vídeo, indicando o comezo e final. Posteriormente, entrénanse os modelos cos diferentes
algoritmos de Intelixencia Artificial, sendo estes Multi-Layer Perceptron e Long Short-Term
Memory Recurrent Neural Networks, e diferentes entradas de características e parámetros. Cada
modelo é comprobado contra datos de diferentes datasets, como o propio deste proxecto
ou o de referencia. Finalmente, se calculan métricas de rendemento para cada modelo, e son
comparadas entre elas.
Palabras chave
Artificial intelligence
MLP
LSTM
Classification
DanceSport
Intelixencia artificial
Clasificación
MLP
LSTM
Classification
DanceSport
Intelixencia artificial
Clasificación
Dereitos
Atribución-NoComercial-SinDerivadas 3.0 España