Automatic recognition of offensive team formation in american football plays

Handle URI:
http://hdl.handle.net/10754/575813
Title:
Automatic recognition of offensive team formation in american football plays
Authors:
Atmosukarto, Indriyati; Ghanem, Bernard ( 0000-0002-5534-587X ) ; Ahuja, Shaunak; Muthuswamy, Karthik; Ahuja, Narendra
Abstract:
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Visual Computing Center (VCC); VCC Analytics Research Group
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
Conference/Event name:
2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013
Issue Date:
Jun-2013
DOI:
10.1109/CVPRW.2013.144
Type:
Conference Paper
ISSN:
21607508
ISBN:
9780769549903
Appears in Collections:
Conference Papers; Electrical Engineering Program; Electrical Engineering Program; Visual Computing Center (VCC); Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; 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.authorAhuja, Shaunaken
dc.contributor.authorMuthuswamy, Karthiken
dc.contributor.authorAhuja, Narendraen
dc.date.accessioned2015-08-24T09:26:55Zen
dc.date.available2015-08-24T09:26:55Zen
dc.date.issued2013-06en
dc.identifier.isbn9780769549903en
dc.identifier.issn21607508en
dc.identifier.doi10.1109/CVPRW.2013.144en
dc.identifier.urihttp://hdl.handle.net/10754/575813en
dc.description.abstractCompared 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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectAmerican football playsen
dc.subjectformationen
dc.subjectrecognitionen
dc.titleAutomatic recognition of offensive team formation in american football playsen
dc.typeConference Paperen
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.journal2013 IEEE Conference on Computer Vision and Pattern Recognition Workshopsen
dc.conference.date23 June 2013 through 28 June 2013en
dc.conference.name2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013en
dc.conference.locationPortland, ORen
dc.contributor.institutionADSC, Singaporeen
dc.contributor.institutionNTU, Singaporeen
dc.contributor.institutionUIUC, United Statesen
kaust.authorGhanem, Bernarden
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