Badminton action recognition based on improved I3D convolutional neural network
Huangnan Zheng, Huichang Shen, Yisong Zhang, Haotian Wang · 2023
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.