Auxiliary Basketball Training System Based on Big Data
Hao Wei · 2021
With the rapid development of computer technology, the world has entered the era of big data. “Big data” needs new processing mode to have stronger decision-making power, insight and process optimization ability to adapt to massive, high growth rate and diversified information assets. The combination of big data and sports events is becoming more and more popular. People have begun to combine big data technology to assist basketball training. The purpose of this paper is to study the design of the auxiliary basketball training system based on big data, so as to improve the success rate of slam dunk by using data. In this paper, the shooting posture parameters of athletes and coaches are obtained by the method of body posture estimation. Firstly, based on the basic principle of Kinect three-dimensional sensor, the sequence color image and its depth map information are collected. Then, a background modeling method based on vibe modeling is proposed. The domain pixels are used to create the background model to study the image features. Finally, a human pose estimation method based on contour features and image processing is proposed to realize the pose estimation of human joints. The experimental investigation shows that the background modeling algorithm based on vibe modeling overcomes the shortcomings of traditional methods, and can achieve better detection effect for dynamic background; the model-based human posture estimation algorithm and the model-free attitude estimation algorithm can extract the main joint data of human body more accurately; and the obtained human body data can guide basketball players to train well, and after the guidance of data information, the success rate of boys increased to 63.33%, and that of girls increased to 62.15%,.