Basketball posture recognition based on HOG feature extraction and convolutional neural network

Basketball posture recognition is one of the important research topics in human-computer interaction and physical education, which is of great significance in medical treatment, sports, security and other aspects. With the development of machine learning, the application value of basketball pose recognition in physical education is becoming more and more extensive. This paper constructs a novel convolutional neural network model to recognize basketball posture. The model consists of 11 layers. Convolution and pooling operations are carried out for five basketball postures in the sampled data set. By fusing with the features extracted from HOG, finer features can be obtained. Finally, the data set is trained and recognized by entering the full connection layer for classification. The results show that compared with the traditional machine learning methods, the recognition performance of new model is better.