Action recognition technology for professional basketball game training based on SSD and 3D convolution

In professional basketball games, athlete action recognition is an important part of sports performance analysis and intelligent assisted training. The complex scene background, frequent target occlusion, and varied motion patterns pose challenges to traditional detection and recognition methods in terms of accuracy and real-time performance. In view of this, the study proposes an improved object detection model for single shot multi-box detectors by combining pyramid feature integration module and dual axis convolutional perception module. At the same time, an action recognition model based on an improved 3D convolutional network is designed through adaptive weight fusion and attention mechanism. In the testing of the object detection model, when the number of iterations reached 500, the average accuracy improved by 6.14% and the frame rate decreased by 8.56%. The missed detection rate under low light conditions was 7.3%, the false detection rate was 8.7%, and the detection time was 30.8 ms. The highest detection accuracy of the action recognition model in complex backgrounds was 89.3%, and the robustness score was 91.9. The results indicate that the proposed model can maintain high accuracy and efficiency in complex backgrounds and fast movements in professional basketball game scenarios. The research model significantly improves the performance and robustness of action recognition, which can provide certain technical support for intelligent sports training systems.