摘要
Abstract
Group dance in university sports dance places high demands on the standardization and coordination of movements,and the evaluation of dance movements primarily relies on the subjective judgment of instructors,which can be easily influenced by personal preferences and subjective factors.To address this,an auxiliary teach-ing method for university sports dance group dance courses based on motion capture technology is proposed.Firstly,the Kinect technology is utilized to acquire human skeleton information and obtain the human silhouette.The depth camera captures the dance movement postures of students,and the human joint points are used as sample sets to construct a human motion model database.Secondly,the feature plane similarity matching method is employed to calculate motion parameters,accurately recording students' dance movements and converting them into feature da-ta,thereby achieving real-time analysis of dance movement postures.Finally,Zernike moments are used to extract features from incorrect dance movements,and the SVM classifier is utilized to obtain the classification results of dance movements,followed by the correction of incorrect dance movements.The experimental results demonstrate that this method can effectively enhance the auxiliary teaching effectiveness of group dance courses in college physi-cal education.关键词
动作捕捉技术/高校体育舞蹈集体舞课程/辅助教学/SVM分类器Key words
motion capture technology/college sports dance collective dance course/auxiliary teaching/SVM clas-sifier分类
信息技术与安全科学
