Improving Table Tennis Training and Technique Analysis: Accurate Classification of Actions with Informer Encoder
Article No.: 27, Pages 1 - 4
Abstract
This paper introduces a binary classification network that utilizes the Informer Encoder to classify ping pong actions as either correct or incorrect. The dataset used in this study comprises 949 action videos capturing two fundamental ping pong stroke actions performed by athletes, including both correct and incorrect actions. The average frame count for each action is 38.62. Temporal skeletal data is extracted from the videos using a 2D pose estimation model, and a fully connected layer is employed to perform binary classification on the temporal skeletal data. During training and testing, the extracted skeletal data is segmented into temporal sequences of 39 frames for training and evaluation. On the test set, the Informer Encoder-based model achieves 100% accuracy, while the MLP-based model reaches 94%.
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- Improving Table Tennis Training and Technique Analysis: Accurate Classification of Actions with Informer Encoder
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Published In
August 2023
378 pages
ISBN:9798400708701
DOI:10.1145/3627341
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 15 December 2023
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- Research-article
- Research
- Refereed limited
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- Supported?by Key program of Hunan Provincial Department of Education
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ICCVIT 2023
ICCVIT 2023: International Conference on Computer, Vision and Intelligent Technology
August 25 - 28, 2023
Chenzhou, China
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ICCVIT '23 Paper Acceptance Rate 54 of 142 submissions, 38%;
Overall Acceptance Rate 54 of 142 submissions, 38%
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