Indoor person behavior recognition based on deep learning and knowledge graph

At present, indoor human behavior recognition technology is widely used in the fields of video intelligent monitoring, intelligence elderly care and intelligent medical care, etc. The existing research methods are more focused on classifying human behavior and ignore the connection between objects in the scene and human behavior. In order to make full use of the association between the objects in the scene and indoor human behavior, this paper proposes an indoor human behavior recognition model based on deep learning and knowledge graph. Firstly, we use YOLOv5 target detection network to identify the objects in the scene, and also use human keypoint detection algorithm to locate the skeletal keypoints of human body, extract and analyze the spatial features of objects and keypoints to obtain the human behavior feature triad, construct the behavior detection knowledge graph and perform search and inference to realize human behavior recognition. The experimental results show that the recognition accuracy of the model is 94.9% on the homemade test set.