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    Recognizing team formation in american football

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    Type
    Article
    Authors
    Atmosukarto, Indriyati
    Ghanem, Bernard cc
    Nasef Saadalla, Mohamed Magdy Mohamed
    Ahuja, Narendra
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    VCC Analytics Research Group
    Date
    2015-01-20
    Online Publication Date
    2015-01-20
    Print Publication Date
    2014
    Permanent link to this record
    http://hdl.handle.net/10754/563273
    
    Metadata
    Show full item record
    Abstract
    Most existing software packages for sports video analysis require manual annotation of important events in the video. Despite being the most popular sport in the United States, most American football 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 takesmanyman hours per week. These two statistics are the building blocks for more high-level analysis such as play strategy inference and automatic statistic generation. In this chapter, 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.
    Citation
    Atmosukarto, I., Ghanem, B., Saadalla, M., & Ahuja, N. (2014). Recognizing Team Formation in American Football. Advances in Computer Vision and Pattern Recognition, 271–291. doi:10.1007/978-3-319-09396-3_13
    Publisher
    Springer Nature
    Journal
    Computer Vision in Sports
    DOI
    10.1007/978-3-319-09396-3_13
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-09396-3_13
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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