Abstract
In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set.
Abstract (translated)
在本文中，我们提出了ASTRA，一种专为足球比赛动作捕捉任务设计的Transformer-based模型。ASTRA解决了该任务和数据集中固有的几个挑战，包括对精确动作局部定位的要求、存在长尾数据分布、某些动作的不可见性以及固有标签噪声。为了实现这一目标，ASTRA采用了以下方法：（a）Transformer编码器-解码器架构以实现所需的输出时间分辨率并产生精确预测；（b）平衡混合策略来处理数据的长期尾分布；（c）一个具有不确定性的迁移头以捕捉标签的可变性；（d）输入音频信号以增强对不可见动作的检测。结果表明，ASTRA取得了很好的效果，在测试集上的平均得分达到了66.82。此外，在足球网络2023年动作捕捉挑战中，ASTRA在挑战集上的平均得分达到了70.21。
URL
https://arxiv.org/abs/2404.01891
