東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Automated Pre-Play Analysis of American Football Formations Using Deep Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automated Pre-Play Analysis of American Football Formations Using Deep Learning./
Author:	Newman, Jacob DeLoy.
Description:	1 online resource (61 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-04.
Contained By:	Masters Abstracts International84-04.
Subject:	Football. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29404949click for full text (PQDT)
ISBN:	9798352648636

Automated Pre-Play Analysis of American Football Formations Using Deep Learning.
Newman, Jacob DeLoy.

Automated Pre-Play Analysis of American Football Formations Using Deep Learning. - 1 online resource (61 pages)

Source: Masters Abstracts International, Volume: 84-04.

Thesis (M.Sc.)--Brigham Young University, 2022.

Includes bibliographical references

Annotation and analysis of sports videos is a time consuming task that, once automated, will provide benefits to coaches, players, and spectators. American football, as the most watched sport in the United States, could especially benefit from this automation. Manual annotation and analysis of recorded video of American football games is an inefficient and tedious process. Currently, most college football programs focus on annotating offensive formation. As a first step to further research for this unique application, we use computer vision and deep learning to analyze an overhead image of a football play immediately before the play begins. This analysis consists of locating and labeling individual football players, as well as identifying the formation of the offensive team. We obtain greater than 90% accuracy on both player detection and labeling, and 84.8% accuracy on formation identification. These results prove the feasibility of building a complete American football strategy analysis system using artificial intelligence.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352648636Subjects--Topical Terms:

643161
Football.
Index Terms--Genre/Form:

542853
Electronic books.

Automated Pre-Play Analysis of American Football Formations Using Deep Learning.
LDR:02250nmm a2200325K 4500 001 2361733
005 20231027101309.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798352648636
035 $a (MiAaPQ)AAI29404949
035 $a (MiAaPQ)BrighamYoung10632
035 $a AAI29404949
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Newman, Jacob DeLoy. $3 3702418
245 1 0 $a Automated Pre-Play Analysis of American Football Formations Using Deep Learning.
264 0 $c 2022
300 $a 1 online resource (61 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-04.
500 $a Advisor: Harrison, Willie;Lundrigan, Philip.
502 $a Thesis (M.Sc.)--Brigham Young University, 2022.
504 $a Includes bibliographical references
520 $a Annotation and analysis of sports videos is a time consuming task that, once automated, will provide benefits to coaches, players, and spectators. American football, as the most watched sport in the United States, could especially benefit from this automation. Manual annotation and analysis of recorded video of American football games is an inefficient and tedious process. Currently, most college football programs focus on annotating offensive formation. As a first step to further research for this unique application, we use computer vision and deep learning to analyze an overhead image of a football play immediately before the play begins. This analysis consists of locating and labeling individual football players, as well as identifying the formation of the offensive team. We obtain greater than 90% accuracy on both player detection and labeling, and 84.8% accuracy on formation identification. These results prove the feasibility of building a complete American football strategy analysis system using artificial intelligence.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Football. $3 643161
650 4 $a Neural networks. $3 677449
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Brigham Young University. $3 1017451
773 0 $t Masters Abstracts International $g 84-04.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29404949 $z click for full text (PQDT)