| Title |
Basketball Trajectory Capture Method based on Neural Network Under the Background of Sports Teaching |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.6.553 |
| Keywords |
Target detection; Trajectory prediction; Neural network |
| Abstract |
Traditional basketball training methods have shortcomings such as insufficient efficiency and low training specificity, and the effectiveness of traditional basketball training strategies is difficult to guarantee. The research on basketball robots can help basketball players practice in daily training, save training costs and improve training efficiency. The field of basketball robot research, it involves many aspects of knowledge learning, such as deep learning, robot kinematics, robot control, etc., which has very high research value and significance. Basketball has the characteristics of fast speed, which brings great difficulty to the research of basketball. According to the real-time and accuracy requirements of basketball robot vision system, aiming at the shortcomings and problems of traditional methods, combined with the current rapid development of neural network method, this paper carries out the research on basketball target detection and rotating ball trajectory prediction based on deep learning. By stacking the neural network, we can achieve the task of predicting the trajectory, and meet the real-time and certain accuracy. The experimental results show that compared with the traditional physical model, the network in this paper has higher anti-interference ability and accuracy. The proposed network has significant real-time advantages in predicting time, and its accuracy in predicting points is closer to the true value. Compared with traditional algorithms, the accuracy has been greatly improved. It can help basketball players practice in daily training, saving training costs and improving training efficiency. In subsequent basketball training, the system in this article can be used for auxiliary training |