6MapNet：通过三元组网络表示来自跟踪数据的足球运动员
6MapNet: Representing soccer players from tracking data by a triplet network
摘要：
虽然足球运动员的个人价值已经成为天文数字，但主观判断仍然在球员分析中发挥着重要作用。最近，已经有新的尝试来使用基于视频的事件流数据来定量地掌握球员的风格。然而，由于高注释成本和事件流数据的稀疏性，它们在可扩展性方面有一些限制。在本文中，我们建立了一个三元组网络命名为6MapNet，可以有效地捕捉球员的运动风格使用在游戏中的GPS数据。在没有任何足球特定动作的注释的情况下，我们使用球员的位置和速度来生成两种类型的热图。然后，我们的子网络将这些热图映射到特征向量，其相似性对应于演奏风格的实际相似性。实验结果表明，该方法可以准确地识别球员只有少量的匹配。
英文摘要：
Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis. Recently, there have been new attempts to quantitatively grasp players' styles using video-based event stream data. However, they have some limitations in scalability due to high annotation costs and sparsity of event stream data. In this paper, we build a triplet network named 6MapNet that can effectively capture the movement styles of players using in-game GPS data. Without any annotation of soccer-specific actions, we use players' locations and velocities to generate two types of heatmaps. Our subnetworks then map these heatmap pairs into feature vectors whose similarity corresponds to the actual similarity of playing styles. The experimental results show that players can be accurately identified with only a small number of matches by our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
