In recent years, badminton has become more and more popular in national fitness programs. Amateur badminton clubs have been established all over the country, and amateur badminton events at all levels have increased significantly. Due to the lack of correct medical supervision and health guidance, many people have varying degrees of injury during sports. Therefore, it is very important to study the method of badminton movement capture and intelligent correction based on machine vision to provide safe and effective exercise plan for amateur badminton enthusiasts. This article aims to study the methods of motion capture and intelligent correction of badminton. Aiming at the shortcoming of the mean shift algorithm that it is easy to lose the target when the target is occluded or the background is disturbed, this paper combines the mean shift algorithm with the Kalman filter algorithm and proposes an improvement to the combined algorithm. The improved algorithm is added to the calculation of the average speed of the target, which can be used as the target speed when the target is occluded to predict the area where the target may appear at the next moment, and it can also be used as a judgment condition for whether the target is interfered by the background. The improved algorithm combines the macroscopic motion information of the target, can overcome the problem of target loss when the target is occluded and background interference, and improves the robustness of target tracking. Using LabVIEW development environment to write the system software of the Japanese standard tracking robot, the experiment verified the rationality and correctness of the improved target tracking algorithm and motion control method, which can meet the real-time performance of moving target tracking. Experimental results show that 83% of amateur badminton players have problems with asymmetric functions and weak links. Based on machine vision technology, it can provide reliable bottom line reference for making training plans, effectively improve the quality of action, improve the efficiency of action, and promote the development of sports competitive level.

中文翻译：

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基于机器视觉的羽毛球运动动作捕捉及智能矫正方法

近年来，羽毛球在全民健身项目中越来越受欢迎。全国各地建立了业余羽毛球俱乐部，各级业余羽毛球赛事明显增多。由于缺乏正确的医疗监督和健康指导，很多人在运动过程中都会出现不同程度的损伤。因此，研究基于机器视觉的羽毛球运动捕捉与智能矫正方法，为业余羽毛球爱好者提供安全有效的锻炼计划显得尤为重要。本文旨在研究羽毛球运动捕捉和智能矫正的方法。针对均值漂移算法在目标被遮挡或背景受到干扰时容易丢失目标的缺点，本文将均值漂移算法与卡尔曼滤波算法相结合，提出了对组合算法的改进。改进后的算法加入了目标平均速度的计算中，可以作为目标被遮挡时的目标速度来预测下一时刻目标可能出现的区域，也可以作为目标是否受到背景干扰的判断条件。改进算法结合了目标的宏观运动信息，克服了目标被遮挡和背景干扰时目标丢失的问题，提高了目标跟踪的鲁棒性。使用LabVIEW开发环境编写日标跟踪机器人系统软件，实验验证了改进目标跟踪算法和运动控制方法的合理性和正确性，能够满足运动目标跟踪的实时性。实验结果表明，83%的业余羽毛球运动员存在功能不对称和薄弱环节的问题。基于机器视觉技术，可为制定训练计划提供可靠的底线参考，有效提高动作质量，提高动作效率，促进体育竞技水平的发展。