VIDEO-BASED TABLE TENNIS TRACKING AND TRAJECTORY PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS

Haoxuan Li, Saba Ghazanfar Ali, Junhao Zhang, Bin Sheng, Ping Li, Younhyun Jung, Jihong Wang, Po Yang, Ping Lu, Khan Muhammad and Lijiuan Mao
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Haoxuan Li: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Saba Ghazanfar Ali: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Junhao Zhang: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Bin Sheng: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Ping Li: ��The Hong Kong Polytechnic University, Hong Kong, China
Younhyun Jung: ��Gachon University, Gyeonggi-do, Korea
Jihong Wang: �Shanghai University of Sport, Shanghai, China
Po Yang: �The University of Sheffield, Sheffield, UK
Ping Lu: ��State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation, Shenzhen, China
Khan Muhammad: *Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Republic of Korea
Lijiuan Mao: �Shanghai University of Sport, Shanghai, China

FRACTALS (fractals), 2022, vol. 30, issue 05, 1-23

Abstract: One of the fascinating aspects of sports rivalry is that anything can happen. The significant difficulty is that computer-aided systems must address how to record and analyze many game events, and fractal AI plays an essential role in dealing with complex structures, allowing effective solutions. In table tennis, we primarily concentrate on two issues: ball tracking and trajectory prediction. Based on these two components, we can get ball parameters such as velocity and spin, perform data analysis, and even create a ping-pong robot application based on fractals. However, most existing systems rely on a traditional method based on physical analysis and a non-machine learning tracking algorithm, which can be complex and inflexible. As mentioned earlier, to overcome the problem, we proposed an automatic table tennis-aided system based on fractal AI that allows solving complex issues and high structural complexity of object tracking and trajectory prediction. For object tracking, our proposed algorithm is based on structured output Convolutional Neural Network (CNN) based on deep learning approaches and a trajectory prediction model based on Long Short-Term Memory (LSTM) and Mixture Density Networks (MDN). These models are intuitive and straightforward and can be optimized by training iteratively on a large amount of data. Moreover, we construct a table tennis auxiliary system based on these models currently in practice.

Keywords: Table Tennis; Deep Learning; Object Tracking; Trajectory; Fractal AI Prediction (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22401569

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