With the growing popularity of tennis globally, there is an increasing demand for intelligent systems capable of accurate action recognition and timely feedback, with potential applications in real-time broadcasting, AI-assisted coaching, skill evaluation, and injury prevention. Traditional approaches, which often rely on manual observation and delayed correction, struggle to meet the needs of fine-grained skill development. This paper presents the Tennis Action Recognition You Only Look Once Detection Network (TAR-YOLO), a novel, pose-driven action recognition model based upon the YOLO11 architecture, addressing challenges such as occlusion, pose deformation, and multi-view consistency. In this research, two novel components RES-Head and DSAM are proposed, while SPD-Conv and Slide Loss are integrated into this model. These four key architectural improvements significantly enhance the performance of TAR-YOLO, with RES-Head enabling multi-scale feature fusion, DSAM enhancing attention-based representation of key motion cues and deformable action patterns, SPD-Conv improving feature extraction, and Slide Loss addressing sample imbalance through dynamic gradient reweighting during training. A custom dataset, TAR-Det, specifically designed for tennis pose estimation and action classification, is also constructed. Experimental results show that TAR-YOLO achieves a Precision of 95.4%, Recall of 93.7%, mAP0.5 of 96.2%, mAP0.5:0.95 of 93.5%, FLOPs of 16.9, and FPS of 89.3 on the TAR-Det dataset, confirming its effectiveness in complex and dynamic tennis action recognition tasks.
© Copyright 2025 Scandinavian Journal of Medicine & Science in Sports. Wiley. Alle Rechte vorbehalten.