Efficient feature recognition and matching technology for IoT‐enabled sports training

Abstract

With the development of the Internet of Things (IoT), the IoT technology is gradually applied to the sport training of athletes. According to the training feature of athletes collected by the IoT equipment, this paper proposes to use the method of feature image sequence analysis and feature extraction to automatically identify the training of athletes. The mathematical model of feature image recognition based on gray difference is established, and the pyramid iterative recognition algorithm is used to reduce the recognition error effectively. In addition, a mathematical model of image sequence feature extraction based on moment invariants is established, and the feature table for athlete matching is discussed in detail. Based on the concept of dynamic establishment of search area and the principle of two‐step template feature recognition and matching, through the analysis of the pictures of high jumpers, the change of the athlete angle of left knee in the process of high jump is obtained, which achieves the purpose of automatic identification of key actions. At the same time, the random error existing in manual recognition is completely eliminated.