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Title:Analiza odbojkarske igre z uporabo algoritmov računalniškega vida in strojnega učenja
Authors:ID Plankelj, Marko (Author)
ID Mlakar, Uroš (Mentor) More about this mentor... New window
Files:.pdf MAG_Plankelj_Marko_2025.pdf (2,97 MB)
MD5: AACA7BE8A125512D60E2F4FEEF4C6C5B
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Sodobne tehnologije v zadnjih letih približujejo šport širšemu krogu ljudi z različnimi interaktivnimi podatki med prenosi, zmanjšajo možnost človeške napake, hkrati pa izboljšujejo rezultate tekmovalcev z analizo med tekmo ali informacijami o področjih, ki jim je smiselno nameniti dodatno pozornost na treningih. Tako smo v magistrskem delu skozi teoretičen in praktičen del s pomočjo razvoja aplikacije, ki temelji na konvolucijskih nevronskih mrežah U-Net in YOLOv8 za zaznavo odbojkarskega igrišča ter sledenju premikanju žoge, predstavili možnost uporabe napredne analize posnetkov odbojkarske tekme. S pomočjo uporabe aplikacije lahko uporabnik spozna priložnosti, ki jih, med drugim tudi v športu, ponuja uporaba sodobnih tehnologije.
Keywords:računalniški vid, konvolucijske nevronske mreže, odbojka, spletna aplikacija
Place of publishing:Maribor
Publisher:[M. Plankelj]
Year of publishing:2025
PID:20.500.12556/DKUM-91552 New window
UDC:004.932.021(043.2)
COBISS.SI-ID:231082755 New window
Publication date in DKUM:01.04.2025
Views:223
Downloads:102
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:14.01.2025

Secondary language

Language:English
Title:Volleyball game analysis using computer vision algorithms
Abstract:In recent years, modern technologies have made sports more accessible to a wider audience by providing interactive data during broadcasts, reducing the risk of human error, and enhancing athletes' performance through real-time analysis and targeted training insights. This master's thesis combines theoretical and practical approaches by developing an application based on U-Net and YOLOv8 convolutional neural networks for volleyball court detection and ball tracking. The project demonstrates the potential of advanced video analysis in sports, allowing users to explore the opportunities modern technology offers in improving sports performance.
Keywords:computer vision, convolutional neural networks, volleyball, web application


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