@inproceedings{784602a70bad420d9cac31b9e921080f,
title = "Computer vision for the ballet industry: A comparative study of methods for pose recognition",
abstract = "The presence of computer vision technology is continually expanding into multiple application domains. An industry and an art form that is particularly attractive for the application of computer vision algorithms is ballet. Due to the well-codified poses, along with the challenges that exist within the ballet domain, automation for the ballet environment is a relevant research problem. The paper proposes a model called BaReCo, which allows for ballet poses to be recognised using computer vision methods. The model contains multiple computer vision pipelines which allows for the comparison of approaches that have not been widely explored in the ballet domain. The results have shown that the top-performing pipelines achieved an accuracy rate of 99.375% and an Equal Error Rate (EER) of 0.119% respectively. The study additionally produced a ballet pose dataset, which serves as a contribution to the ballet and computer vision community. By combining suitable computer vision methods, the study demonstrates that successful recognition of ballet poses can be accomplished.",
keywords = "Automation, Ballet industry, Computer vision, Dance, Pose recognition",
author = "Margaux Fourie and {van der Haar}, Dustin",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 23rd International Conference on Business Information Systems, BIS 2020 ; Conference date: 08-06-2020 Through 10-06-2020",
year = "2020",
doi = "10.1007/978-3-030-53337-3_9",
language = "English",
isbn = "9783030533366",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer",
pages = "118--129",
editor = "Witold Abramowicz and Gary Klein",
booktitle = "Business Information Systems - 23rd International Conference, BIS 2020, Proceedings",
address = "Germany",
}