Abstract Pose estimation and tracking in sports has gained significant attention due to its potential to revolutionize performance analysis, injury prevention, and strategic decision-making. This survey presents a comprehensive overview of the methodologies, datasets, challenges, and future directions in this rapidly evolving field. We explore traditional approaches, including geometric and statistical models, and highlight the transformative impact of deep learning techniques, such as convolutional neural networks, transformers, and hybrid architectures, which have enabled highly accurate and robust pose estimation. The paper also discusses dataset creation and ground-truthing techniques tailored to sports contexts, emphasizing the importance of multimodal data, scalability, and representativeness. Applications across diverse sports, from individual to team-based activities, demonstrate the versatility of pose estimation systems in both real-time and offline settings. However, challenges such as occlusions, dynamic backgrounds, and computational efficiency persist, necessitating further innovation. We identify future research directions, including the integration of multimodal data, edge computing, and ethical considerations, to enhance accuracy, interpretability, and generalizability. This survey aims to provide a foundational reference for researchers and practitioners, fostering advancements in pose estimation and tracking technologies that meet the unique demands of sports analytics.
Uçar et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: