Badminton action recognition based on improved I3D convolutional neural network

Huangnan Zheng; Huichang Shen; Yisong Zhang; Haotian Wang

doi:10.1117/12.2684732

21 July 2023 Badminton action recognition based on improved I3D convolutional neural network

Huangnan Zheng, Huichang Shen, Yisong Zhang, Haotian Wang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 1271731 (2023) https://doi.org/10.1117/12.2684732
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

In order to solve the issue of inaccurate recognition of time series of badminton action by 2D networks and the high cost of using skeleton data for badminton action recognition, this article aims to explore the use of RVM (Robust Video Matting) to extract the silhouette of individuals in videos, combined with an improved I3D network (referred to as the SI3D network) for action classification in badminton matches. We collected a dataset of badminton match videos featuring multiple players and labeled them according to different action categories. By using the SI3D network for training, this article achieved good performance on the test set, with an accuracy of 91.5%. This study demonstrates the effectiveness of the I3D network in badminton action classification and provides a new research direction for action recognition in sports.

Citation Download Citation

Huangnan Zheng, Huichang Shen, Yisong Zhang, and Haotian Wang "Badminton action recognition based on improved I3D convolutional neural network", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 1271731 (21 July 2023); https://doi.org/10.1117/12.2684732

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KEYWORDS

Convolution

Video

Convolutional neural networks

Action recognition

3D modeling

Feature extraction

Motion models

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