TESA‐Net: A Court‐Aware Architecture for Flow‐Free Basketball Action Recognition

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.