The article discusses the challenges faced in accurately recognizing human actions in skeleton-based action recognition and proposes an innovative model called STG-NODE to address these challenges. The model utilizes dynamic time warping and custom ordinary differential equations to improve the influence of intraindividual action differences and long-term temporal dependencies. Extensive experiments demonstrate the superior performance of STG-NODE in action recognition and provide new ideas for future development in this field.
