Training a group of badminton serving machines to reproduce a rally
This paper proposes to train a group of badminton serving machines by a simple deep neural network (DNN) consisting of 8 dense layers. Our ultimate goal is to have the proposed system reproduce a badminton rally captured from social media such as YouTube. All the parameters of these serving machines need to be predicted from a trajectory image in a race video through a deep learning regression model. We can successfully estimate 10 trajectory parameters with an average RMSE loss of 0.087.
I. Introduction
II. System Model
III. Reproducing One Shot by Regression
IV. Experimental Results
V. Conclusions
© Copyright 2020 International Conference on Pervasive Artificial Intelligence (ICPAI), Taipei, Taiwan, 2020. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | technical and natural sciences sport games |
| Tagging: | künstliche Intelligenz deep learning maschinelles Lernen neuronale Netze |
| Published in: | International Conference on Pervasive Artificial Intelligence (ICPAI), Taipei, Taiwan, 2020 |
| Language: | English |
| Published: |
2020
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| Online Access: | https://ieeexplore.ieee.org/document/9302654 |
| Document types: | congress proceedings |
| Level: | advanced |