Skeleton-based human action recognition with context awareness

Human action recognition is the process of automatically recognizing human activities in digital video sequences. It is an important research topic in computer vision, particularly in the field of video surveillance. Conventional approaches for skeleton-based action recognition mainly focus on modeling the temporal evolution of the skeleton data, while not taking the contextual information of the scene into consideration. In this paper, we propose a novel human action recognition framework with context awareness: YOLOv5 is used for context recognition first, then the obtained information is integrated with ST-GCN for skeleton-based behavior recognition. Experimental results show the superiority of our algorithm over ST-GCN alone in industrial scenes.