Badminton Action Classification Based on PDDRNet

DOI

10.2991/978-94-6463-230-9_118How to use a DOI?

Keywords

badminton action classification; human pose estimation; model lightweight; knowledge distillation

Abstract

Badminton is one of the most popular sports nowadays. To assist badminton teaching, a two-stage badminton movement classification method based on PDDRNet is proposed in this paper. In the first stage, the PDDRNet model for human pose estimation is trained using the knowledge distillation architecture of the teacher student network, the student network uses the lightweight model SECANet, while SimCC is simultaneously applied to replace the heatmap for representation. In the second stage, the estimated poses from the first stage are used for feature engineering, and XGBOOST is applied to classify the underlying badminton movements. In order to verify the performance of our proposed algorithm, we leverage the MPII datasets for human pose estimation experiments, and a proprietary badminton movement dataset for badminton movement classification. The results show that on the MPII dataset, it achieves a 3.1% improvement in PCKh when compared to lite-HRNet. In the second stage, the accuracy of the badminton movement classification algorithm using XGBOOST reaches 93.5%, which is 7.60% higher than the KNNbased badminton movement classification method.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xian-Wei Zhou
AU  - Le Ruan
AU  - Song-Sen Yu
AU  - Jian Lai
AU  - Zheng-Feng LI
AU  - Wei-Tao Chen
PY  - 2023
DA  - 2023/09/04
TI  - Badminton Action Classification Based on PDDRNet
BT  - Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023)
PB  - Atlantis Press
SP  - 980
EP  - 987
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-230-9_118
DO  - 10.2991/978-94-6463-230-9_118
ID  - Zhou2023
ER  -