Compared to security surveillance and military applications, where automated action analysis is prevalent, the sports domain is extremely under-served. Most existing software packages for sports video analysis require manual annotation of important events in the video. American football is the most popular sport in the United States, however most game analysis is still done manually. Line of scrimmage and offensive team formation recognition are two statistics that must be tagged by American Football coaches when watching and evaluating past play video clips, a process which takes many man hours per week. These two statistics are also the building blocks for more high-level analysis such as play strategy inference and automatic statistic generation. In this paper, we propose a novel framework where given an American football play clip, we automatically identify the video frame in which the offensive team lines in formation (formation frame), the line of scrimmage for that play, and the type of player formation the offensive team takes on. The proposed framework achieves 95% accuracy in detecting the formation frame, 98% accuracy in detecting the line of scrimmage, and up to 67% accuracy in classifying the offensive team's formation. To validate our framework, we compiled a large dataset comprising more than 800 play-clips of standard and high definition resolution from real-world football games. This dataset will be made publicly available for future comparison. © 2013 IEEE.
|Original language||English (US)|
|Title of host publication||2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|State||Published - Jun 2013|