Graph Neural Networks for Skeleton-based action recognition

Abstract

 One of the important directions of the application of artificial intelligence based on human bone behavior recognition is also a research hotspot in the field of computer vision in recent years. Human image video not only contains complex backgrounds, but also uncertain factors such as changes in illumination and changes in the appearance of the human body, which makes behavior recognition based on image videos have certain limitations. Compared with image video, human skeleton video can well overcome the influence of these uncertain factors, so be- havior recognition based on human skeleton has received more and more attention. The human skeleton sequence not only contains the tempo- ral features, but also the spatial structure features of the human body. How to effectively extract the discriminative spatial and temporal fea- tures from the human skeleton sequence is a problem to be solved. In recent years, many methods have been applied to bone-based behavior recognition, such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Graph Neural Network (GCN). This article will introduce the content and characteristics of these three methods one by one. , And conduct a comparative analysis on it.