Abstract
The presence of computer vision technology is continually expanding into multiple application domains. With the increased availability of new camera and sensor technologies, it is possible to gather the necessary data required to apply computer vision in multiple environments. The task of recognising human activities using technology is one of the primary applications of computer vision. A human activity 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. This dissertation considers the use of computer vision methods to recognise different ballet poses. A literature review is done first to determine if it is a valid problem and explore the current methods used within ballet. Accordingly, the study proposes a model called BaReCo for ballet pose recognition using computer vision, which serves as a guide for the implementation of the prototype system. A specialised benchmark is then created to assess various facets of the model. The developed solution effectively makes use of a captured dataset, which was created for this study. The implemented computer vision pipelines contain various stages including pre-processing, localisation, feature extraction and classification. Results are consequently derived from the prototype to address the benchmark. The results of the benchmark for the BaReCo implementation, show that the study accomplishes the objective of recognising ballet poses using computer vision methods. Some key findings indicate that closely related poses are the potential cause for errors in recognition. The results also reveal that the use of novel deep learning techniques such as OpenPose and neural networks, along with traditional classification approaches, yield promising results. The study additionally provides a ballet pose dataset which serves as a contribution to the ballet and computer vision community. Future work for the study includes making improvements to the current implementation, as well as the application of other computer vision approaches on images and video data. The prototype system has validated the use of computer vision in the ballet domain to achieve pose recognition successfully.
M.Sc. (Computer Science)