Human Action Recognition Based on Vision Transformer and L2 Regularization

Abstract

In recent years, the field of human action recognition has been the focus of computer vision, and human action recognition has a good prospect in many fields, such as security state monitoring, behavior characteristics analysis and network video image restoration. In this paper, based on attention mechanism of human action recognition method is studied, in order to improve the model accuracy and efficiency in VIT network structure as the framework of feature extraction, because video data includes characteristics of time and space, so choose the space and time attention mechanism instead of the traditional convolution network for feature extraction, In addition, L2 weight attenuation regularization is introduced in model training to prevent the model from overfitting the training data. Through the test on the human action related dataset UCF101, it is found that the proposed model can effectively improve the recognition accuracy compared with other models.