Image optical processing based on convolutional neural networks in sports video recognition simulation

The demand of sports video recognition simulation is increasing, but the traditional methods have some limitations in dealing with optical problems. Therefore, the purpose of this paper is to improve the effect of image optical processing by using convolutional neural networks. This paper first analyzes the structure of convolutional neural networks commonly used in computer vision applications, and discusses the improved method of convolutional neural networks to better understand and represent human motion in motion videos. Based on the process analysis of sports video recognition results, the concrete steps of image optical processing are completed. The advantages of convolutional neural network in image optical processing are demonstrated by simulation experiments on some sports videos and comparison with traditional methods. The experimental results show that the image optical processing based on convolutional neural network has a high recognition rate and can be used as an effective auxiliary means for sports training. By accurately analyzing and understanding human movements in sports videos, coaches and trainers can provide more effective training programs tailored to the needs of individual athletes. This can lead to improved performance results and better overall results.