本文提出了一种基于深度学习的击剑动作识别系统设计方案。该系统采用mediapipe Pose模型和OpenCV模型提取人体骨骼角度信息并使用KNN算法进行人体姿势识别。在数据清洗方面本文采用了数据预处理和数据平衡技术。系统界面采用tkinter标准GUI库编程语言为PythonIDE为PyCharm。实验结果表明该系统能够准确识别击剑动作。该方案可为击剑运动员提供姿势识别和技术分析支持。翻译摘要为

This article proposes a design scheme for a fencing action recognition system based on deep learning. The system uses mediapipe Pose model and OpenCV model to extract human skeleton angle information, and uses KNN algorithm for human posture recognition. In terms of data cleaning, this article uses data preprocessing and data balancing techniques. The system interface uses the tkinter standard GUI library, the programming language is Python, and the IDE is PyCharm. The experimental results show that the system can accurately recognize fencing actions. This scheme can provide posture recognition and technical analysis support for fencing athletes