Journal
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Volume 37, Issue 5, Pages 6275-6285Publisher
IOS PRESS
DOI: 10.3233/JIFS-179209
Keywords
Deep learning; neural network; trajectory; recognition algorithm; prediction model
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Funding
- Topics of Guangdong Sports Bureau: analysis of the technical and tactical characteristics of Guangdong table tennis player Liu Shiwen's main opponents in the Rio Olympic Games [GDSS 2016133]
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Ball sports have great variability in the game and the intelligent control of the rules of ball movement can effectively improve the training effect of athletes. However, the current research on artificial intelligence of spherical motion trajectory prediction points is basically blank. Based on this, this study is based on deep learning technology, and obtains the main experimental data through network data collection in the research and builds the table tennis spatial position image data set under various environments with accurate annotation based on the traditional deep learning. At the same time, the convolutional neural network is used as the location recognition algorithm, and a prediction algorithm for predicting the trajectory of table tennis is proposed based on the recurrent neural network. In addition, this paper designs comparative experiments to analyze the effectiveness of the algorithm model, and evaluates the real-time recognition, location and trajectory prediction capabilities, and conducts quantitative analysis. The research shows that the algorithm has certain practical effects and can provide theoretical reference for subsequent related research.
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