A topic model approach to represent and classify american football plays

J. Varadarajan, I. Atmosukarto, S. Ahuja, B. Ghanem, N. Ahuja
British Machine Vision Conference, (2013)

A topic model approach to represent and classify american football plays

Keywords

american football plays

Abstract

​We address the problem of modeling and classifying American Football offense teams’ plays in video, a challenging example of group activity analysis. Automatic play classification will allow coaches to infer patterns and tendencies of opponents more efficiently, resulting in better strategy planning in a game. We define a football play as a unique combination of player trajectories. We develop a framework that uses player trajectories as inputs to MedLDA, a supervised topic model. The joint maximization of both likelihood and inter-class margins of MedLDA in learning the topics allows us to learn semantically meaningful play type templates, as well as, classify different play types with 70% average accuracy. Furthermore, this method is extended to analyze individual player roles in classifying each play type. We validate our method on a large dataset comprising 271 play clips from real-world football games, which will be made publicly available for future comparisons.

Code

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