FoT: an efficient transformer framework for real-time small object detection in football videos

Football videos are playing an increasingly important role in event analysis and tactical evaluation within computer vision. Traditional object detection methods, relying on region proposals and anchor generation, struggle to balance real-time performance and accuracy in complex scenarios such as multi-view, motion blur, and small object recognition. Meanwhile, Transformer-based methods face challenges in capturing fine-grained target information due to their high computational cost and slow training convergence. To address these problems, we propose a novel end-to-end detection framework-Football Transformer (FoT). By introducing the Local Interaction Aggregation Unit (LIAU) and Multi-Scale Feature Interaction Module (MFIM), FoT achieves an efficient balance between global semantic expression and local detail capture. Specifically, LIAU reduces the self-attention computation complexity from [Formula: see text] to O(N) through feature aggregation within local windows and a window offset mechanism. MFIM strengthens the collaborative expression of low-level details and high-level semantics through multi-scale feature alignment and progressive fusion, effectively integrating low-level details and high-level semantics, significantly improving small object detection performance. Experimental results show that FoT achieves a 3.0% mAP improvement over the best baseline on the Soccer-Det dataset and a 1.3% gain on the FIFA-Vid dataset, while maintaining real-time inference speed. These results validate the effectiveness and robustness of the proposed method under complex football video scenarios.