A Topic Model Approach to Representing and Classifying Football Plays

Jagannadan Varadarajan, Indriyati Atmosukarto, Shaunak Ahuja, Bernard Ghanem, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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 ef- ficiently, resulting in better strategy planning in a game. We define a football play as a unique combination of player trajectories. To this end, we develop a framework that uses player trajectories as inputs to MedLDA, a supervised topic model. The joint maximiza- tion 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.
Original languageEnglish (US)
Title of host publicationProcedings of the British Machine Vision Conference 2013
PublisherBritish Machine Vision Association and Society for Pattern Recognition
ISBN (Print)1901725499
StatePublished - Jan 10 2014


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