Research on human behavior serialization recognition based on skeleton graph

In view of the fact that the traditional method is not expanding well in human behavior recognition, this paper proposes a serialization research idea. A sequence which can represent the dynamic action is generated by using SVM to train and recognize static action whose feature vectors of skeleton map extracts from Kinect. Therefore, as long as the static action library is rich, a variety of dynamic actions can be identified, and it has good scalability. In order to reduce the influence of the error recognition of static motion, this paper proposes an error correction algorithm based on front and back information. Experiments show that the algorithm has higher recognition accuracy, and has better robustness and real-time.