Application of Badminton Sequence Image Processing Based on Feature Extraction Algorithm

With the rapid development of computer technology, the application of computer information technology in the field of physical education is becoming increasingly widespread, especially in promoting students’ physical health, showing great potential. This paper focuses on badminton teaching in universities, and from the perspective of improving students’ physical health, deeply studies the application practice of badminton sequence image processing technology based on feature extraction algorithm in teaching. Through feature extraction algorithms, subtle differences in swing movements can be captured, providing a reliable data foundation for subsequent motion analysis. In order to further improve processing efficiency, this paper adopts the idea of multi process programming and implements a parallel processing mechanism for classification software design. This innovation not only significantly shortens the image processing time, but also greatly improves the overall detection efficiency of the system, providing real-time and efficient feedback for badminton teaching. The speed change of the racket during the swing can reflect the athlete’s strength control and the explosive power at the moment of hitting the ball. By calculating the position changes of the racket in consecutive image frames, the velocity curve can be obtained to analyze the trend of velocity changes. Acceleration is the rate of change in velocity, which better reflects the athlete’s power output at the moment of hitting the ball. Calculating acceleration can further reveal the strength and explosiveness of the swing motion. In order to verify the effectiveness of the proposed method, this paper conducted experimental simulations using Matlab and implemented automatic classification and detection of badminton swing quality levels through the VC6.0 programming environment. The experimental results show that the badminton sequence image processing technology based on a feature extraction algorithm can accurately identify swing movements at different levels, providing a scientific evaluation basis and personalized teaching suggestions for badminton teaching in universities.