Play type recognition in real-world football video

This paper presents a vision system for recognizing the sequence of plays in amateur videos of American football games (e.g. offense, defense, kickoff, punt, etc). The system is aimed at reducing user effort in annotating football videos, which are posted on a web service used by over 13,000 high school, college, and professional football teams. Recognizing football plays is particularly challenging in the context of such a web service, due to the huge variations across videos, in terms of camera viewpoint, motion, distance from the field, as well as amateur camerawork quality, and lighting conditions, among other factors. Given a sequence of videos, where each shows a particular play of a football game, we first run noisy play-level detectors on every video. Then, we integrate responses of the play-level detectors with global game-level reasoning which accounts for statistical knowledge about football games. Our empirical results on more than 1450 videos from 10 diverse football games show that our approach is quite effective, and close to being usable in a real-world setting.