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.