Analysis of Volleyball Tactics and Movements Based on 3D Spatio-Temporal Residual Network.

Sports technology and 3D motion recognition require models to be able to accurately identify athletes' movements, which is crucial for training analysis, game strategy development and refereeing assistance decisions. To maintain a high recognition rate under different competition scenes, different athlete styles and different environmental conditions, and to ensure the practicality and reliability of the model, two independent 3D convolutional neural networks are applied to construct the action recognition model of Two-stream 3D Residual Networks. According to the temporal-spatial characteristics of human movements in video, the model introduces attention mechanism and combines time dimension to build a Two-stream 3D Residual Networks action recognition model integrating time-channel attention. The average accuracy of Top-1 and Top-5 action recognition models of Two-stream 3D Residual Networks integrated with pre-activation structure is 68.97% and 91.68%. The residual block of the pre-activated structure can enhance the model's effectiveness. The average precision of Top-1 and Top-5 of action recognition model of two-stream 3D spatio-temporal residual network integrating time-channel attention is 85.73% and 92.05%, which has higher accuracy. The action recognition model of the two-stream 3D spatio-temporal residual network, which integrates time-channel attention, is accurate and achieves good recognition results with volleyball action recognition in real scenes. [ABSTRACT FROM AUTHOR]