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master's thesis
Praćenje lica pomoću dubokog učenja
2023. urn:nbn:hr:168:528009
Krišković, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Krišković, M. (2023). Praćenje lica pomoću dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:528009

Krišković, Marko. "Praćenje lica pomoću dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:528009

Krišković, Marko. "Praćenje lica pomoću dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:528009

Krišković, M. (2023). 'Praćenje lica pomoću dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 13 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:528009

Krišković M. Praćenje lica pomoću dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 November 13] Available at: https://urn.nsk.hr/urn:nbn:hr:168:528009

M. Krišković, "Praćenje lica pomoću dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:528009

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Metadata
TitlePraćenje lica pomoću dubokog učenja
Title (english)Face Tracking Based on Deep Learning
AuthorMarko Krišković
MentorIgor Sunday Pandžić (mentor)
Committee memberIgor Sunday Pandžić (predsjednik povjerenstva)
Committee memberLea Skorin-Kapov (član povjerenstva)
Committee memberMirko Sužnjević (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-10, Croatia
Abstract
Zadatak je bio napraviti algoritam praćenja lica koji radi na principu CNN-a i usporediti ga s jednim od tradicionalnih algoritama praćenja lica. Model CNN-a koji sam uzeo u ovu svrhu je YOLOv8, a software koji sam uzeo za uspoređivanje je Visage|SDK. Označio sam bazu podataka za treniranje i istrenirao YOLOv8. Usporedio sam performansu algoritama na slikama i snimkama. U ishodu uspoređivanja utvrdio sam da oba algoritma imaju svoje nedostatke i prednosti te da se oba mogu specijalizirati za različita područja. YOLO je bolji za praćenje detalja individualnih komponenti lica, a Visage|SDK za praćenje oblika cijelokupnog lica.
Abstract (english)
The task was to create a face tracking algorithm that works on the CNN principle and compare it with one of the traditional face tracking algorithms. The CNN model I took for this purpose is YOLOv8, and the software I took for comparison is Visage|SDK. I marked the training database and trained YOLOv8. I compared the performance of algorithms on images and recordings. As a result of the comparison, I found that both algorithms have their own advantages and disadvantages, and that both can specialize in different areas. YOLO is better for tracking the details of individual facial components, and Visage|SDK is better for tracking the shape of the entire face.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:528009
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-10-11 09:26:12