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[{"key": "dc.contributor.advisor", "value": "Meht\u00e4l\u00e4, Saana", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Kettunen, Aku", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2023-02-02T05:57:29Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2023-02-02T05:57:29Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2023", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/85282", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "T\u00e4ss\u00e4 tutkimuksessa tarkastellaan konen\u00e4\u00f6n nykytilaa joukkueurheilun kontekstissa kirjallisuuskatsauksena. Konen\u00e4k\u00f6 on koneoppimisen alalaji, jolla pyrit\u00e4\u00e4n tuottamaan informaatiota video- tai still-kuvista. Konen\u00e4k\u00f6 on ottanut viime vuosina harppauksia teknologian kehittymisen ja tietokoneiden parantuneen laskentatehon ansiosta. T\u00e4m\u00e4 on luonut painetta konen\u00e4\u00f6n hy\u00f6dynt\u00e4miseen my\u00f6s urheilun automaattisessa analysoinnissa. T\u00e4rkeimm\u00e4t videosta analysoitavat asiat ovat pelaajien ja peliv\u00e4lineen sijainti suhteessa pelikentt\u00e4\u00e4n, sek\u00e4 pelaajien identiteetti. T\u00e4st\u00e4 paikkatietodatasta saadaan jalostettua huomattava m\u00e4\u00e4r\u00e4 erilaista informaatiota, joka on hy\u00f6dyksi sek\u00e4 pelaajille, valmennukselle ett\u00e4 urheilun kuluttajille. Konen\u00e4\u00f6n avulla saatu paikkatietodata on erilaisiin pelaajiin kiinnitett\u00e4viin sensoreihin perustuviin j\u00e4rjestelmiin verrattuna ihannetilanteessa halvempi ja helpompi toteuttaa. Konen\u00e4k\u00f6j\u00e4rjestelm\u00e4t ovat kuitenkin t\u00e4h\u00e4n menness\u00e4 kyenneet analysoimaan vain spesifej\u00e4 lajeja ja perustuvat yleens\u00e4 useaan kameraan. Pelaajien paikan tunnistaminen on huomattavasti helpompaa kuin peliv\u00e4lineen tunnistus. Pelaajien identifiointi taas on eritt\u00e4in vaikea haaste, jota ei ole luotettavasti saatu ratkaistua. Parhaat kaupalliset konen\u00e4k\u00f6j\u00e4rjestelm\u00e4t ovat laajasti k\u00e4yt\u00f6ss\u00e4 eri lajeissa, ja ovat ratkaisseet koen\u00e4\u00f6n ongelmia maailman johtavissa joukkueurheilulajeissa. N\u00e4ill\u00e4 j\u00e4rjestelmill\u00e4 on automatisoitu eri lajien, kuten jalkapallo, j\u00e4\u00e4kiekko ja koripallo, analysointia. K\u00e4yt\u00e4nn\u00f6ss\u00e4 kaikki johtavat j\u00e4rjestelm\u00e4t perustuvat konvoluutioneuroverkkoihin, joka kertoo syv\u00e4oppimisen voimasta kuvien ja videon k\u00e4sittelyss\u00e4. Konen\u00e4k\u00f6j\u00e4rjestelm\u00e4, joka soveltuu usean lajin analysoimiseen, on viel\u00e4 kaukana, eik\u00e4 sellaisen toteuttaminen ole v\u00e4ltt\u00e4m\u00e4tt\u00e4 j\u00e4rkev\u00e4\u00e4, sill\u00e4 eri lajien analysoijalle tuomat vaatimukset ovat niin erilaisia.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "This thesis examines the present applications of computer vision in the domain of team sports as a literature review. Computer vision is a subset of machine learning that focuses on extracting information from images and videos. Computer vision has taken strides in the last years due to improved technology and the improved computational power of computers. This has brought pressure for autonomous analysis of sports videos with the help of computer vision. The most important pieces of data extracted from the sports videos include player position, ball position and player identity. From these three sets of data large amounts of useful information can be refined for players, coaches, and sports fans. In optimal circumstances, data acquisition by computer vision can be easier and cheaper to implement than sensor-based approaches, but computer vision has so far been implemented to very specific tasks and sports, hence lacking a general application. These systems are often based on multiple cameras that can be costly and complicated to use. Locating players is much easier than locating the ball. Hardest challenge so far has been identifying players and is yet to be solved effectively in most cases. Best commercial applications are widely used in the worlds\u2019 largest team sports. These systems have been used to automate the analysis of sports like basketball, football, and ice hockey. Practically all these systems use convolutional neural networks as the basis of their computer vision models which tells about the power of these deep learning networks when dealing with images and videos. A computer vision system that can analyze multiple different sports is a distant dream and executing one might not necessarily be useful.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2023-02-02T05:57:29Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2023-02-02T05:57:29Z (GMT). No. of bitstreams: 0\n Previous issue date: 2023", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "26", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "CNN", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Konen\u00e4k\u00f6 joukkueurheilun kontekstissa", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "bachelor thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202302021565", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Bachelor's thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Kandidaatinty\u00f6", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietoj\u00e4rjestelm\u00e4tiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Information Systems Science", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_7a1f", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "bachelorThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "601", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "syv\u00e4oppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "konen\u00e4k\u00f6", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "teko\u00e4ly", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "joukkueurheilu", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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