Weak Supervised Behavior Recognition Algorithm Based on Domain Knowledge Graph

The use of artificial intelligence technology for technical and tactical analysis has become an important means and method in karate competitions and training. The integration of artificial intelligence technology and sports technical and tactical analysis is an important way to innovate and improve the technical and tactical level. In this paper, a knowledge graph in the karate domain is designed. In order to effectively use the domain knowledge graph, a new two-flow graph convolutional network is adopted, which includes classifier branch and attribute feature branch. The attribute feature branch maps the attribute embedding and score of each video instance to an attribute feature space. The semantic word embedding vectors of all concepts are used as the input of the classifier branch, and finally a classifier for behavior classification is generated. Finally, the generated classifier is evaluated according to the attribute characteristics of each video, and the whole network is optimized by the classification loss pair. In addition, the time information of the video is modeled by the attention mechanism. This technology effectively solves the problems of high labor cost, serious data loss, long delay, and low accuracy in traditional methods. The results show that the new model has a significant effect on improving the accuracy of behavior recognition, and it also lays a foundation for intelligent technical and tactical analysis. basis.