<scp>TESA</scp> ‐Net: A Court‐Aware Architecture for Flow‐Free Basketball Action Recognition

ABSTRACT Human action recognition in sports videos is a challenging computer vision task due to fast motion, frequent occlusions and fine‐grained visual similarities among action classes. This work presents TESA‐Net (Temporal‐Efficient Spatial Attention Network), an efficient dual‐stream architecture for basketball action recognition that achieves state‐of‐the‐art performance while maintaining computational efficiency. Unlike existing methods that rely on expensive 3D convolutions or full spatio‐temporal attention mechanisms, TESA‐Net employs a pre‐trained 2D ResNet‐50 backbone with lightweight temporal aggregation. The key innovation is a novel Court Line Detection module that augments the appearance stream with edge‐based geometric features, enabling accurate discrimination between shot types that differ primarily in shooting distance. We evaluate TESA‐Net on two complementary benchmarks: Basketball‐51, which targets fine‐grained shot classification in professional broadcasts, and MultiSubjects, which addresses coarse‐grained action recognition in amateur gymnasium recordings. On Basketball‐51, TESA‐Net achieves a validation accuracy of 94.37%, surpassing the previous state‐of‐the‐art HAQT (92.76%) by 1.61 percentage points. On MultiSubjects, TESA‐Net reaches 96.12% accuracy, matching transformer‐based approaches while using significantly fewer parameters. Owing to its efficient design, TESA‐Net requires substantially less memory than competing 3D‐based methods, enabling practical deployment without high‐end hardware.