【摘要】 针对现有体能考核中俯卧撑计数裁判负荷大、效率低的问题,设计一种基于机器视觉的俯卧撑计数算法。该算法使用改进的YOLOv3进行人体目标检测,由Ultralight-SimplePose预测出俯卧撑时人体的关键点分布,之后利用SVM分类器,训练SVM模型,将俯卧撑的几种阶段分类,进行计数。测试结果表明,该算法人体关键点识别率可以达到0.945,俯卧撑计数准确率大于99%,可以准确实现体能训练或考核中的人体俯卧撑姿态识别,并进行计数功能。更多还原

【Abstract】 In order to solve the problems of heavy load and low efficiency of push-up counting referee in the existing physical fitness assessment, a push-up counting algorithm based on machine vision is designed. The system uses improved YOLOv3 to detect human targets, and then uses Ultralight-SimplePose to predict the distribution of the key points of the human body during push-ups. After that, SVM classifier is used to train SVM model, and several stages of push-ups are classified and counted. The test results show that the recognition rate of key points of human body can reach 0.945, and the deviation of push up count is less than 1%. It can accurately realize the recognition of push-up posture in physical training or examination, and carry out the counting function.更多还原