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Title:
Določanje uspešnosti počepa s pomočjo strojnega vida
Authors:
ID
Graj, Nejc
(Author)
ID
Fister, Iztok
(Mentor)
More about this mentor...
ID
Vrbančič, Grega
(Comentor)
Files:
VS_Graj_Nejc_2024.pdf
(3,43 MB)
MD5: 0F41AF0095760FE4113B85DCE0672521
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V tem diplomskem delu bomo predstavili področje strojnega učenja, bolj specifično področje globokega učenja. V teoretičnem delu bomo prikazali, kako se je strojno učenje do sedaj že uporabljalo v športu, kako strojno in globoko učenje delujeta ter kako poteka proces učenja konvolucijskih nevronskih mrež. V praktičnem delu bomo ustvarili svojo učno množico in nato z algoritmom, ki je zasnovan na konvolucijskih nevronskih mrežah, ustvarili model, ki je zmožen določati uspešnost počepa po pravilih zveze za Powerlifting.
Keywords:
strojni vid
,
globoko učenje
,
konvolucijske nevronske mreže
,
powerlifting
Place of publishing:
Maribor
Publisher:
[N. Graj]
Year of publishing:
2024
PID:
20.500.12556/DKUM-88841
UDC:
004.8796.88(043.2)
COBISS.SI-ID:
220206083
Publication date in DKUM:
19.09.2024
Views:
0
Downloads:
27
Metadata:
Categories:
KTFMB - FERI
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GRAJ, Nejc, 2024,
Določanje uspešnosti počepa s pomočjo strojnega vida
[online]. Bachelor’s thesis. Maribor : N. Graj. [Accessed 27 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=88841
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Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
29.05.2024
Secondary language
Language:
English
Title:
Determining squat depth with the help of machine vision
Abstract:
In this diploma thesis, we will delve into the field of machine learning, specifically focusing on the area of deep learning. In the theoretical part, we will examine how machine learning has been until now used in sports and as well as how machine learning, deep learning, and the process of training convolutional neural networks work. In the practical part, we will create our own training dataset and use an algorithm based on convolutional neural networks to create a model capable of assessing the performance of a squat according to the rules of the International Powerlifting Federation.
Keywords:
machine learning
,
deep learning
,
convolutional neural networks
,
powerlifting
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