Basketball motion recognition and tracking method based on improved convolutional neural network

To improve the accuracy of basketball motion analysis, this study proposes a basketball motion recognition and tracking method based on an improved convolutional neural network. This method combines an intelligent sensor system with an improved dual-mode convolutional neural network to identify basketball motion steps; A tracking method based on the Northeast sky coordinate system was proposed to depict the motion trajectory of basketball players. The experimental results show that the average recognition accuracy of the improved convolutional neural network model is 99.3 %, which is superior to K-nearest neighbors and other models. This model structure can better capture the complexity and diversity of basketball footwork, improve recognition accuracy, and enhance generalization ability, while still maintaining high recognition accuracy in the face of new movements. The average error of linear trajectory tracking is 4.3 %, while the average errors of curved trajectory tracking in the X, Y, and Z directions are 4.1 %, 5.9 %, and 6.1 %, respectively. Research has shown that this method provides an effective approach for basketball analysis and training, which helps to improve the competitive level of basketball players.