The massive growth of data collection in sports has opened numerous avenues for professional teams and media houses to gain insights from this data. The data collected includes per frame player and ball trajectories, and event annotations such as passes, fouls, cards, goals, etc. Graph Convolutional Networks (GCNs) have recently been employed to process this highly unstructured tracking data which can be otherwise difficult to model because of lack of clarity on how to order players in a sequence and how to handle missing objects of interest. In this thesis, we focus on the goal of automatic event detection from football videos. We show how to model the players and the ball in each frame of the video sequence as a graph, and present the results for graph convolutional layers and pooling methods that can be used to model the temporal context present around each action.

翻译：体育领域数据收集的大规模增长为专业团队和媒体单位从这些数据中获得洞察力开辟了许多渠道,收集的数据包括每个框架播放器和球轨,以及诸如传球、污点、卡片、目标等事件说明。 图表革命网络(GCNs)最近被用来处理这一高度结构化的跟踪数据,这些数据本来可能难以建模,因为不清楚如何按顺序命令玩家和如何处理缺失的感兴趣对象。在这个论文中,我们侧重于从足球视频中自动检测事件的目标。我们展示了如何在视频序列的每个框中将玩家和球作为图解,并展示了图形革命层的结果以及可用于模拟每个行动的时间背景的集合方法。