Construction and optimization method of modern dance movement style feature classification model based on deep learning

Human gesture estimation and action recognition are the current research hotspots in the field of computer vision.In this paper, we propose a dance pose estimation method based on multiple dilation convolution with hybrid attention and a dance action recognition algorithm based on spatio-temporal map convolution.In the pose estimation, the residual module is used to reduce the computational load of the network, while the LAM module is employed to fuse different dance features to improve the model's representation of effective features.The GAM module is then utilized to superimpose the global feature information, capturing richer joint point information.The graph attention mechanism is embedded in the action recognition algorithm to achieve better neighbor aggregation, followed by the construction of a new partitioning strategy to assign different weights to the limbs, which enhances the recognition ability of the model.The gesture estimation algorithm in this paper reduces the number of parameters by 36.73% compared with the Cross Former model, and is able to realize high accuracy reconstruction of modern dance movements such as jumping, lowering, kicking, and stretching, etc.The ST-GCN converges faster than the PA-LSTM, and the convergence trend is more stable.The recognition accuracies in ten modern dance movement style features are 90% and above, which shows that the design of the dance gesture estimation method and the dance movement recognition algorithm in this paper achieves the task of estimating and recognizing modern dance movement style features in real time.