Abstract
Analyzing dual-lane speed climbing videos provides critical insights into data-driven performance evaluation in sports climbing. This study introduces an enhanced deep learning approach based on 3D ResNets to classify and analyze speed climbing states. Leveraging an annotated dataset of 872 high-resolution videos covering 15 state combinations, the model integrates optimized 3D convolutions and residual connections, achieving significant improvements in classification accuracy and computational efficiency. With a test accuracy of 92.78%, the model significantly outperforms 2D CNNs and C3D models. Additionally, its lightweight architecture and reduced computational complexity equip it with the potential for real-time deployment in controlled environments. While challenges such as data imbalance and limited generalization remain, this research provides a robust technical framework for speed climbing video analysis and lays the groundwork for broader applications in spatiotemporal modeling and intelligent sports analytics.