Research on Feature Extraction Method of Aerobics Jumping Movement Based on AdaBoost Algorithm and Gaussian Mixture Model - Zhang, Qianqian; Lin, Xian (Informatica, 2025)
Aiming at the problems of sensitive background interference, insufficient key frame recognition accuracy and low computational efficiency of traditional aerobics jumping action feature extraction methods, this study proposes a feature extraction method that integrates Gaussian mixture model, entropy sequence fusion and AdaBoost algorithm. The video key frames are extracted by machine vision technology, combined with entropy sequence (standard deviation ± 0.3) and music energy features (threshold = 0.85) to achieve synchronized key frame recognition (96.8% accuracy); Gaussian mixture model is used to eliminate background noise (42% reduction in false detection rate), combined with Harris3D operator to construct the action potential function, and integrated with AdaBoost algorithm to integrate the weak classifier to optimize feature extraction. The experiments show that compared with the existing SOTA method, the average error of azimuth recognition is 1.2° (significantly lower than 3.8°±1.1° in A-BLSTM and 4.5°±1.5° in NN-BIGRU, p