An intelligent recognition method of athletes' human body movements based on PCA-LBP algorithm

To achieve accurate recognition of athletes' movements during different sports processes, an intelligent recognition method for athletes' human body movements based on PCA-LBP algorithm is proposed. Firstly, CCD sensors are used to capture athletes' movement images, and the images are subjected to discrete wavelet transform enhancement processing. Then, the bilateral filtering method is used to remove the noise. Subsequently, we employ the local binary patterns (LBP) technique to capture the distinct characteristics of athletes' body motions, followed by dimensionality reduction utilising principal component analysis (PCA). By meticulously scrutinising the sequential attributes of both training and test datasets, we ascertain whether the detected motion patterns align with those from the training cohort, facilitating intelligent identification. The empirical outcomes underscore the method's efficacy in significantly enhancing image clarity, bolstering the precision of athletes' body motion recognition to a level consistently exceeding 96%.