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Support learning vovinam exercises based on computer vision

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dc.contributor.advisor Phan, Duy Hùng
dc.contributor.author Phạm, Sơn Tùng
dc.contributor.author Thái, Thành Đô
dc.contributor.author Phạm, Hồng Giang
dc.date.accessioned 2023-02-15T02:21:16Z
dc.date.available 2023-02-15T02:21:16Z
dc.date.issued 2022
dc.identifier.uri http://ds.libol.fpt.edu.vn/handle/123456789/3621
dc.description.abstract Computer vision has many applications which has attracted many researchers, especially with the problems of recognizing actions, postures, movements. This Thesis offers a method to support students to perform correct postures during martial arts practice. We have collected and labeled data about the movements of a traditional Vietnamese martial art called Vovinam. In the original paper of ST-GCN, before input into the model, we need to transform videos to the sequence of keypoint positions by frame to be handled in the next phase; it seems to be that normally the transform phase didn't reach effective performance. Therefore, the purpose of this thesis is to improve the ST-GCN model in terms of input. Firstly, we use a sequence of recently released techniques to extract the skeleton and its key points from the input video. Then, the sequence of keypoint positions by frame will be inputted into the deep learning architecture based on the ST-GCN model and the output will be the determined action. On our dataset, adding the input processing stage to the recognition model has yielded much better results than applying the original model. The final accuracy is 99.23%, showing that the model has the potential to be applied in practice. en_US
dc.language.iso en en_US
dc.publisher FPTU HN en_US
dc.subject Computer Science en_US
dc.subject Artificial Intelligence en_US
dc.subject Computer Vision en_US
dc.subject ST-GCN en_US
dc.subject Martial Art en_US
dc.subject Vovinam en_US
dc.title Support learning vovinam exercises based on computer vision en_US
dc.title.alternative Hỗ trợ học tập bài luyện vovinam dựa trên thị giác máy tính en_US
dc.type Thesis en_US


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