Research on Visual Image Recognition of Dance Movements Based on Deep Computer Learning

This study investigates the application of computer deep learning in visual recognition of dance movements, focusing particularly on the utilization of Graph Convolutional Networks (GCN) and adaptive attention mechanisms in skeleton behavior recognition. By integrating the OpenPose algorithm for human pose estimation and employing the Spatio-Temporal Graph Convolutional Network (ST-GCN) model, efficient recognition of dance actions is achieved. Transfer learning techniques are employed to optimize the model training process, thereby enhancing recognition accuracy. The results demonstrate that deep learning-based dance movement recognition technology performs admirably in complex environments, offering significant practical value for dance education and motion capture in virtual reality.