Abstract We propose a unsupervised learning framework for automatically labeling events in a basketball game. Our framework uses the the optical player tracking data in the NBA. We first learn the time series of defensive assignments using a novel player and location dependent attraction based model which uses hidden Markov models (HMMs), Gaussian processes, and a “bond breaking” model for changes in defensive assignments. Next, we use the learned defensive assignments as an input to a set of HMMs that automatically detect events such as ball screens, drives and post-ups. We show that our models provide significant improvements over existing benchmarks both on defensive assignments and event detection.

中文翻译：

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使用基于能量的防御分配的HMM在篮球中自动进行事件检测

摘要我们提出了一种用于自动标记篮球比赛中事件的无监督学习框架。我们的框架使用光学球员跟踪NBA中的数据。我们首先使用新颖的球员和基于位置的基于吸引力的模型来学习防御任务的时间序列，该模型使用隐藏的马尔可夫模型（HMM），高斯过程和“债券打破”模型来防御任务的变化。接下来，我们将学习到的防御性分配用作一组HMM的输入，这些HMM会自动检测诸如球幕，发球和后仰等事件。我们表明，我们的模型在防御任务和事件检测方面都比现有基准进行了重大改进。