Multi-classification of Human Action Based on ResNet

Abstract

With the development of machine learning and other related technologies, more and more excellent research on human action recognition and classification has been proposed, which significantly promotes the application of this technology in the actual situation. This paper mainly focuses on the characteristics of ResNet18, ResNet50, ResNet101, and ResNet152 in 15 human action recognition and classification tasks respectively. First, adjust and enhance the sample images of all training sets and test sets, and adjust the overall parameters according to the characteristics of the input image, so that all images can be normalized and high quality can be guaranteed at the same time; Then use the above four ResNet models to study and test with an epoch of 200, and explore the characteristics of the four models in this project by comparing the accuracy, loss, confusion matrix graphic, F1-score, and the number of epoch experienced in reaching the steady state of accuracy. The results show that in the overall effect, with the increase of the number of layers of the ResNet models, the accuracy changes in a positive correlation, and the epoch number experienced by the accuracy reaching the steady state changes in a negative correlation.