Learning spatio-temporal context for basketball action pose estimation with a multi-stream network.

Accurate athlete pose estimation in basketball is crucial for game analysis, player training, and tactical decision-making. However, existing pose estimation methods struggle to effectively address common challenges in basketball, such as motion blur, occlusions, and complex backgrounds. To tackle these issues, this paper proposes a basketball action pose estimation framework, which first leverages a multi-dimensional data stream network to extract spatial, temporal, and contextual information separately.