Application of action and posture feature information matching algorithm in college aerobics

With the increasingly rich recreational activities of college students, diversified learning needs and complex physical education resources bring challenges to college physical education. In order to optimize the teaching effect of calisthenics in colleges and universities, this paper proposes a matching method of posture features based on dynamic time warping. Firstly, the dynamic time warping algorithm is introduced, and then the matching model of posture features of calisthenics is constructed on this basis. Finally, the application effect of the model is tested and analyzed. The results show that the model can capture the video frame accurately, and its matching accuracy reaches 94.8%, which greatly improves the accuracy of aerobics action recognition. Good posture matching effect is conducive to teachers to obtain a clear learning situation of students, and provide a reference for adjusting the teaching progress and teaching methods of calisthenics. Under the teaching mode of this model, the average professional score of the students in calisthenics reaches 85 points, which is 25 points higher than that under the convolutional neural network model. It also proves the validity and feasibility of this method in the course of calisthenics in colleges and universities, which is beneficial to enhance the physical quality of college students and enrich the content of calisthenics teaching.