A topic model approach to represent and classify american football plays

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

Research output: Contribution to conferencePaper

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.

Original languageEnglish (US)
DOIs
StatePublished - Jan 1 2013
Event2013 24th British Machine Vision Conference, BMVC 2013 - Bristol, United Kingdom
Duration: Sep 9 2013Sep 13 2013

Other

Other2013 24th British Machine Vision Conference, BMVC 2013
CountryUnited Kingdom
CityBristol
Period9/9/139/13/13

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Varadarajan, J., Atmosukarto, I., Ahuja, S., Ghanem, B., & Ahuja, N. (2013). A topic model approach to represent and classify american football plays. Paper presented at 2013 24th British Machine Vision Conference, BMVC 2013, Bristol, United Kingdom. https://doi.org/10.5244/C.27.64