Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network

Abstract

This study proposes a 3D attitude estimation algorithm using the RMPE algorithm coupled with a deep neural network that combines human pose estimation and action recognition, which provides a new idea for basketball auxiliary training. Compared with the traditional single-action recognition method, the present method makes the recognition accuracy better and the display effect more intuitive. The flipped classroom teaching mode based on this algorithm is applied to the college sports basketball optional course to explore the influence of this teaching mode on the classroom teaching effect. Compared with the evaluation index of action recognition, the experimental results of various action recognition methods and datasets are compared and analyzed, and it is verified that the method has a good recognition effect. The values of Topi and Top5 of the proposed method are 42.21% and 88.77%, respectively, which are 10.61% and 35.09% higher than those of the Kinetics-skeleton dataset. However, compared with the NTU RGM dataset, the recognition rate of Topi is significantly reduced. Compared with the traditional single-action recognition method, this method has better recognition accuracy and a more intuitive display effect. The fusion method of human posture estimation and motion recognition provides a new idea for basketball auxiliary training.