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Volume: 33 | Article ID: art00004
Automatic Annotation of American Football Video Footage for Game Strategy Analysis
  DOI :  10.2352/ISSN.2470-1173.2021.6.IRIACV-303  Published OnlineJanuary 2021

Annotation and analysis of sports videos is a challenging task that, once accomplished, could provide various benefits to coaches, players, and spectators. In particular, American Football could benefit from such a system to provide assistance in statistics and game strategy analysis. Manual analysis of recorded American football game videos is a tedious and inefficient process. In this paper, as a first step to further our research for this unique application, we focus on locating and labeling individual football players from a single overhead image of a football play immediately before the play begins. A pre-trained deep learning network is used to detect and locate the players in the image. A ResNet is used to label the individual players based on their corresponding player position or formation. Our player detection and labeling algorithms obtain greater than 90% accuracy, especially for those skill positions on offense (Quarterback, Running Back, and Wide Receiver) and defense (Cornerback and Safety). Results from our preliminary studies on player detection, localization, and labeling prove the feasibility of building a complete American football strategy analysis system using artificial intelligence.

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Jacob Newman, Jian-Wei Lin, Dah-Jye Lee, Jen-Jui Liu, "Automatic Annotation of American Football Video Footage for Game Strategy Analysisin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2021,  pp 303-1 - 303-7,

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