Basketball action recognition by fusing video recognition techniques with an SSD target detection algorithm

Abstract In the digital era, computer vision technology has become a key tool in the field of sports analysis, especially widely used in basketball action recognition (BAR) technology. To improve the recognition accuracy and efficiency of technical actions in basketball, a BAR system that integrates three-dimensional convolutional neural network video recognition technology and a single-shot multibox detector (SSD) target detection algorithm is proposed. First, the basketball player’s action sequence is captured by the video recognition technique, and then the SSD target detection algorithm is utilized for real-time target detection and localization of the player’s key parts. By combining these two techniques, it is possible to more accurately recognize and classify a wide range of basketball player’s actions including but not limited to shoot, dribbling, passing, and defending. The experimental results indicated that the proposed model of the study provided the best recognition effect in continuous video images with 16 frames, as well as faster response and convergence speeds, and displayed good stability in the computational process. In the application effect comparison experiments with the comparison algorithms, the average recognition rate of the proposed model was 89.5 and 92.5% on the original and cropped frames, respectively, and the average accuracy mean value was 91.69%. Except for the shoot action, the recognition misjudgment rate for the remaining five basketball actions was the lowest among the three methods, which significantly improved the accuracy and effectiveness of action recognition. In addition, the accuracy of the proposed model in practical applications reached 94.50%, and the real-time performance reached 95 ms/frame, both significantly better than the other two compared models, indicating that the model is suitable for real-time BAR tasks and can provide efficient and accurate results.