Research on Table Tennis Swing Recognition Based on Lightweight OpenPose

Aiming to address the issues of irregular movements and inconsistent teacher standards in the teaching process of table tennis swing, the paper proposes a table tennis swing movement recognition system based on an improved Lightweight OpenPose algorithm. The videos or pictures taken by the practitio...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings (International Conference on Advanced Computer Theory and Engineering) pp. 207 - 212
Main Authors: Chen, Peng, Shen, Qingwei
Format: Conference Proceeding
Language:English
Published: IEEE 15.09.2023
Subjects:
Computational modeling
Data models
Education
improved OpenPose algorithm
pose estimation
Reliability
Skeleton
Solid modeling
table tennis swing recognition
Time series analysis
ISSN:2154-7505
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Aiming to address the issues of irregular movements and inconsistent teacher standards in the teaching process of table tennis swing, the paper proposes a table tennis swing movement recognition system based on an improved Lightweight OpenPose algorithm. The videos or pictures taken by the practitioners are preprocessed to extract the frame images of the key movements and reconstruct the human skeleton. The Lightweight OpenPose model was used in place of the OpenPose model, and MobileNet v3-small was utilized instead of the MobileNet v1 network within the Lightweight OpenPose model. Additionally, the InceptionTime time series classification network is utilized to perform action classification and compare it with the standard action, thereby correcting the learner's posture. The improved algorithm achieves a recognition accuracy of over 92.25% for table tennis swinging behavior, surpassing the accuracy of the original model. Furthermore, following the implementation of the lightweight modifications, the model becomes smaller in size and possesses fewer parameters, leading to a significant improvement in the computational speed of the model.
ISSN:2154-7505
DOI:10.1109/ICACTE59887.2023.10335442