论文标题
一种基于图形的方法，用于使用无监督的玩家分类进行足球动作发现
A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification
论文作者
论文摘要
足球视频中的动作发现是确定游戏发生某些关键动作的特定时间的任务。最近，它引入了大量关注，并引入了强大的方法。动作发现涉及了解游戏的动态，事件的复杂性以及视频序列的变化。鉴于他们的模型利用了序列的全局视觉特征，大多数方法都集中在后者上。在这项工作中，我们将重点放在（a）识别和代表图中的玩家，裁判和守门员的节点上，以及（b）将其时间相互作用建模为图的序列。对于播放器识别或玩家分类任务，我们在注释的基准测试中获得了97.72％的精度。对于动作发现任务，我们的方法通过将其与其他视听方式相结合，获得了57.83％的平均图。这种性能超过了基于图的类似方法，并通过繁重的计算方法具有竞争性结果。代码和数据可在https://github.com/ipcv/soccer_action_spotting上找到。
Action spotting in soccer videos is the task of identifying the specific time when a certain key action of the game occurs. Lately, it has received a large amount of attention and powerful methods have been introduced. Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. Most approaches have focused on the latter, given that their models exploit the global visual features of the sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification, or player classification task, we obtain an accuracy of 97.72% in our annotated benchmark. For the action spotting task, our method obtains an overall performance of 57.83% average-mAP by combining it with other audiovisual modalities. This performance surpasses similar graph-based methods and has competitive results with heavy computing methods. Code and data are available at https://github.com/IPCV/soccer_action_spotting.
