Design of Basketball Player Training Action Error Correction System Based on Convolutional Neural Network Algorithm

Abstract

With the development of industry and the progress of science and technology, more and more new technology is gradually applied to the movement of error correction. This not only relieves workers of unneeded burdens by making their task more straightforward and error-free, but it also improves production efficiency. Deep neural networks are one of these new technologies that have exploded in popularity in recent years, with applications in a variety of industries. Of course, the application in image recognition must not be less, image recognition technology based on deep neural network has become more mature, and the error rate of recognition is now much lower than human vision recognition. So at present, some industrial detection is gradually from human vision detection to computer vision detection. This study discloses a basketball action error correction method based on deep learning image recognition, which includes the following steps: receiving each frame of basketball image captured from the fitness video, recording the corresponding time of each frame of fitness image, and preprocessing each frame of fitness image; the preprocessed basketball image was fed into the human joint recognition model, and the human joint recognition model calculated each human joint in the fitness image and output its position coordinates. According to the coordinate position of each joint orderly line, the human skeleton diagram is obtained; the human skeleton diagram is compared and assessed in accordance with standard fitness action, and the nonstandard basketball image is generated to realize basketball action repair. A basketball action error correction system based on deep learning picture identification is also disclosed in the invention. The system and method are capable of efficiently addressing the difficult challenge of comparing fitness movements with and without music rhythm.