1 Taekwondo pose estimation with deep learning architectures on one-dimensional and two-dimensional data / Dinh Duc Luong, Vuong Quang Phuong, Hoang Do Thanh Tung // .- 2023 .- Tập 39 - Số 4 .- P. 343-368 .- 005

This study extracts images from Taekwondo videos and generates skeleton data from frames using the Fast Forward Moving Picture Experts Group (FFMPEG) technique using MoveNet. After that, we use deep learning architectures such as Long Short-Term Memory Networks, Convolutional Long Short-Term Memory, and Long-term Recurrent Convolutional Networks to perform the poses classification tasks in Taegeuk in Jang lessons. This work presents two approaches. The first approach uses a sequence skeleton extracted from the image by Movenet. Second, we use sequence images to train using video classification architecture. Finally, we recognize poses in sports lessons using skeleton data to remove noise in the image, such as background and extraneous objects behind the exerciser. As a result, our proposed method has achieved promising performance in pose classification tasks in an introductory Taekwondo lesson.