Recognizing team formation in american football

Handle URI:
http://hdl.handle.net/10754/563273
Title:
Recognizing team formation in american football
Authors:
Atmosukarto, Indriyati; Ghanem, Bernard ( 0000-0002-5534-587X ) ; Nasef Saadalla, Mohamed Magdy Mohamed; Ahuja, Narendra
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Visual Computing Center (VCC); VCC Analytics Research Group
Publisher:
Springer Science + Business Media
Journal:
Advances in Computer Vision and Pattern Recognition
Issue Date:
2014
DOI:
10.1007/978-3-319-09396-3_13
Type:
Article
ISSN:
21916586
Appears in Collections:
Articles; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAtmosukarto, Indriyatien
dc.contributor.authorGhanem, Bernarden
dc.contributor.authorNasef Saadalla, Mohamed Magdy Mohameden
dc.contributor.authorAhuja, Narendraen
dc.date.accessioned2015-08-03T11:44:36Zen
dc.date.available2015-08-03T11:44:36Zen
dc.date.issued2014en
dc.identifier.issn21916586en
dc.identifier.doi10.1007/978-3-319-09396-3_13en
dc.identifier.urihttp://hdl.handle.net/10754/563273en
dc.description.abstractMost 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.en
dc.publisherSpringer Science + Business Mediaen
dc.titleRecognizing team formation in american footballen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.contributor.departmentVCC Analytics Research Groupen
dc.identifier.journalAdvances in Computer Vision and Pattern Recognitionen
dc.contributor.institutionAdvanced Digital Sciences Center (ADSC)Singapore, Singaporeen
dc.contributor.institutionUniversity of Illinois Urbana-Champaign (UIUC)Champaign, United Statesen
kaust.authorGhanem, Bernarden
kaust.authorNasef Saadalla, Mohamed Magdy Mohameden
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