Advancements in basketball action recognition: Datasets, methods, explainability, and synthetic data applications

Abstract

Basketball Action Recognition (BAR) has received increasing attention in the fields of computer vision and artificial intelligence, serving as a fundamental component in performance evaluation, automated game annotation, tactical analysis, and referee decision-making support. Despite notable advancements driven by deep learning approaches, BAR remains a challenging task due to the inherent complexity of basketball movements, frequent occlusions, and limited availability of standardized benchmark datasets. This survey provides a comprehensive and structured synthesis of current developments in BAR research, encompassing four principal dimensions: dataset curation, computational methodologies, synthetic data generation, and model explainability. A critical analysis of publicly available basketball-specific datasets is presented, delineating their modalities, annotation strategies, action taxonomies, and representational scope. Furthermore, the survey offers a structured classification of state-of-the-art action recognition methodologies, ranging from video-based and skeleton-based models to sensor-driven and multimodal fusion approaches, emphasizing architectural characteristics, evaluation protocols, and task-specific adaptations. The role of synthetic data is systematically examined as a means to address data scarcity, reduce annotation noise, and enhance model generalization through controlled variability and simulation-based augmentation. In parallel, the integration of explainable artificial intelligence (XAI) techniques is also analyzed, with a focus on post-hoc attribution methods, probabilistic reasoning models, and interpretable neural architectures, aimed at improving the transparency and accountability of decision-making processes. The survey identifies persisting research challenges, including dataset heterogeneity, limitations in cross-domain transferability, and the accuracy-interpretability trade-off in deep models. By delineating current limitations and prospective directions, this work provides a foundational reference to guide the development of robust, generalizable, and explainable BAR systems for deployment in real-world sports intelligence applications.