Towards Automated Key-Point Detection in Images with Partial Pool View

Sports analytics has been an up-and-coming field of research among professional sporting organizations and academic institutions alike. With the insurgence and collection of athlete data, the primary goal of such analysis is to improve athletes' performance in a measurable and quantifiable mann...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org
Main Authors: Woinoski, T J, Bajic, I V
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 11.08.2022
Subjects:
Athletes
Swimming
ISSN:2331-8422
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Sports analytics has been an up-and-coming field of research among professional sporting organizations and academic institutions alike. With the insurgence and collection of athlete data, the primary goal of such analysis is to improve athletes' performance in a measurable and quantifiable manner. This work is aimed at alleviating some of the challenges encountered in the collection of adequate swimming data. Past works on this subject have shown that the detection and tracking of swimmers is feasible, but not without challenges. Among these challenges are pool localization and determining the relative positions of the swimmers relative to the pool. This work presents two contributions towards solving these challenges. First, we present a pool model with invariant key-points relevant for swimming analytics. Second, we study the detectability of such key-points in images with partial pool view, which are challenging but also quite common in swimming race videos.
AbstractList Sports analytics has been an up-and-coming field of research among professional sporting organizations and academic institutions alike. With the insurgence and collection of athlete data, the primary goal of such analysis is to improve athletes' performance in a measurable and quantifiable manner. This work is aimed at alleviating some of the challenges encountered in the collection of adequate swimming data. Past works on this subject have shown that the detection and tracking of swimmers is feasible, but not without challenges. Among these challenges are pool localization and determining the relative positions of the swimmers relative to the pool. This work presents two contributions towards solving these challenges. First, we present a pool model with invariant key-points relevant for swimming analytics. Second, we study the detectability of such key-points in images with partial pool view, which are challenging but also quite common in swimming race videos.
Author Bajic, I V
Woinoski, T J
Author_xml – sequence: 1
  givenname: T
  surname: Woinoski
  middlename: J
  fullname: Woinoski, T J
– sequence: 2
  givenname: I
  surname: Bajic
  middlename: V
  fullname: Bajic, I V
BookMark eNotj01LwzAch4MoOOc-gLeA59a8NC89jul0OLCH4nX8m6aa0SXapFa_vQU9_Q4PPA-_K3Tug7cI3VCSF1oIcgfDt_vKGSM6J0IW9AwtGOc00wVjl2gV45EQwqRiQvAF2tZhgqGNeD2mcIJkW_xsf7IqOJ_wvU3WJBc8dh7vTvBmI55cescVDMlBj6sQevzq7HSNLjroo1397xLV24d685TtXx53m_U-A8FkRk3DiABluAKubAsaaGeg5LotG2nKGUgNreJScMobwYpOio5y24CQXWn4Et3-aT-G8DnamA7HMA5-Lh6YInS-qYnkvwpDTkc
ContentType Paper
Copyright 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID 8FE
8FG
ABJCF
ABUWG
AFKRA
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
HCIFZ
L6V
M7S
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
DOI 10.48550/arxiv.2208.05641
DatabaseName ProQuest SciTech Collection
ProQuest Technology Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials
ProQuest Central
Technology collection
ProQuest One
ProQuest Central Korea
SciTech Premium Collection
ProQuest Engineering Collection
Engineering Database
ProQuest Central Premium
ProQuest One Academic
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
Engineering Collection
DatabaseTitle Publicly Available Content Database
Engineering Database
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest Engineering Collection
ProQuest One Academic UKI Edition
ProQuest Central Korea
Materials Science & Engineering Collection
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
Engineering Collection
DatabaseTitleList Publicly Available Content Database
Database_xml – sequence: 1
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Physics
EISSN 2331-8422
Genre Working Paper/Pre-Print
GroupedDBID 8FE
8FG
ABJCF
ABUWG
AFKRA
ALMA_UNASSIGNED_HOLDINGS
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
FRJ
HCIFZ
L6V
M7S
M~E
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
ID FETCH-LOGICAL-a526-1cb205a7c37a37eda8a1fca938d9b6c97c368ad7365313b524f65f13eba56f9c3
IEDL.DBID BENPR
IngestDate Mon Jun 30 09:18:18 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a526-1cb205a7c37a37eda8a1fca938d9b6c97c368ad7365313b524f65f13eba56f9c3
Notes SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
OpenAccessLink https://www.proquest.com/docview/2701331806?pq-origsite=%requestingapplication%
PQID 2701331806
PQPubID 2050157
ParticipantIDs proquest_journals_2701331806
PublicationCentury 2000
PublicationDate 20220811
PublicationDateYYYYMMDD 2022-08-11
PublicationDate_xml – month: 08
  year: 2022
  text: 20220811
  day: 11
PublicationDecade 2020
PublicationPlace Ithaca
PublicationPlace_xml – name: Ithaca
PublicationTitle arXiv.org
PublicationYear 2022
Publisher Cornell University Library, arXiv.org
Publisher_xml – name: Cornell University Library, arXiv.org
SSID ssj0002672553
Score 1.8034307
SecondaryResourceType preprint
Snippet Sports analytics has been an up-and-coming field of research among professional sporting organizations and academic institutions alike. With the insurgence and...
SourceID proquest
SourceType Aggregation Database
SubjectTerms Athletes
Swimming
Title Towards Automated Key-Point Detection in Images with Partial Pool View
URI https://www.proquest.com/docview/2701331806
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LSwMxEA7aKnjyjY9acvAa2002jz2JjxaLWoIWqSdJNllYqLu1uy36703WrR4ELx7DBBImk8lk8uUbAE4jGxmMhUbCHVYopNRtKWO7yHU3NmHEBRnVSt_x4VCMx5GsE25FDatc-sTKUZs89jnyDuYuWHEG2GXn0zfkq0b519W6hMYqaHqmsrABmpe9oXz4zrJgxl3MTL6eMyvyro6avaeLM4w9iJKyMPjlhKuTpb_53zltgaZUUzvbBis22wHrFaIzLnZBf1QBYgt4MS9zF5ZaA2_tB5J5mpXw2pYVBCuDaQYHr86nFNBnZKH0lqQmUOb5BD650ffAqN8bXd2gumgCUhQzFMQad6niMeGKcGuUUEESq4gIE2kWR07AhDKcMLf5iKY4TBhNAmK1oiyJYrIPGlme2QMABQ-Vu-0w7cmsDaZKiCTkQjOjE0_rdQhaS6281IZfvPyo5Ohv8THYwP4ngWeXDVqgUc7m9gSsxYsyLWbteh3bHor56FpycC-fPwFBWKol
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V07T8MwED5BAcHEW7zxAGNoY8ePDAghSkVFqTJUqEyVEztSJUigCa8fxX_kHFoYkNg6MNuybH935_P58x3AUWhDQ6mKPYWHlRdwjiplbMPD7samgqGTUSHdkd2u6vfDaAY-Jn9hHK1yYhMrQ23yxMXI61Sis4IC2BBnj0-eqxrlXlcnJTS-xOLavr_ila04bTcR32NKW5e9iytvXFXA05wKz09i2uBaJkxqJq3RSvtpokOmTBiLJMQGobSRTKB0spjTIBU89ZmNNRdpmDAcdhbmApR1VYO5qH0T3X0HdaiQ6KKzr9fTKldYXY_ehi8nlDrOJheB_8vmVwdZa_mfbcEKLl0_2tEqzNhsDRYqvmpSrEOrV9F9C3L-XObodFtDcE5elA-zkjRtWRHMMjLMSPsBLWZBXLyZRE5P9D2J8vye3OJiN6A3jZlvQi3LM7sFRMlA411OxC5Vt6FcK5UGUsXCxKlLWrYNexMQBmO1LgY_COz83XwIi1e9m86g0-5e78ISdX8mXB5dfw9q5ejZ7sN88lIOi9HBWIQIDKaM2CfepgQj
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Towards+Automated+Key-Point+Detection+in+Images+with+Partial+Pool+View&rft.jtitle=arXiv.org&rft.au=Woinoski%2C+T+J&rft.au=Bajic%2C+I+V&rft.date=2022-08-11&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422&rft_id=info:doi/10.48550%2Farxiv.2208.05641