A Transformer-based System for Action Spotting in Soccer Videos

He Zhu, Junwei Liang, Chengzhi Lin, Jun Zhang, Jianming Hu

Abstract: Action Spotting in the broadcast soccer game is important to understand salient actions and video summary applications. In this paper, we propose an efficient transformer-based system for action spotting in soccer videos. We first use the multi-scale vision transformer to extract features from the videos. Then we adopt a sliding window strategy to further utilize temporal features and enhanced temporal understanding. Finally, the features are input to NetVLAD++ model to obtain the final results. Our model can learn a hierarchy of robust representations and perform well in the Action Spotting Task of SoccerNet Challenge 2022. Our method achieves excellent results and outperforms the baseline and previous published works.