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undergraduate thesis
Vizualno praćenje gibajućih objekata pomoću dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:396133
Bučar, Lovro
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Bučar, L. (2022). Vizualno praćenje gibajućih objekata pomoću dubokog učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:396133

Bučar, Lovro. "Vizualno praćenje gibajućih objekata pomoću dubokog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:396133

Bučar, Lovro. "Vizualno praćenje gibajućih objekata pomoću dubokog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:396133

Bučar, L. (2022). 'Vizualno praćenje gibajućih objekata pomoću dubokog učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:396133

Bučar L. Vizualno praćenje gibajućih objekata pomoću dubokog učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 December 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:396133

L. Bučar, "Vizualno praćenje gibajućih objekata pomoću dubokog učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:396133

Metadata
TitleVizualno praćenje gibajućih objekata pomoću dubokog učenja
Title (english)Visual Moving Object Tracking via Deep Learning
AuthorLovro Bučar
MentorIvan Petrović (mentor)
Committee memberIvan Petrović (član povjerenstva)
Committee memberBorna Bićanić (član povjerenstva)
Committee memberKarlo Koledić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog rada bio je upoznati se s neuronskom mrežom dubokih afiniteta (eng. Deep Affinity Network, DAN), istražiti kako funkcionira i, konačno, testirati ju lokalno sa zadanim podatcima. Istraživanje smo započeli čitanjem rada u kojem se detaljno opisuje funkcioniranje mreže DAN i uobičajene pristupe praćenju objekata u videu. Nakon toga smo preuzeli program s priloženog GitHub repozitorija i pokrenuli testiranje algoritma s videima iz izazova za praćenje objekata. Testiranjem i uspored̄ivanjem modela DAN s ostalim algoritmima koji rade isti posao saznali smo da se naš model ističe s najboljim performansama u najbitnijim mjerilima.
Abstract (english)
The aim of this thesis was to get acquainted with a neural network called Deep Affinity Network (DAN), understand how it works and, finally, test it locally with given datasets. We started the research with reading a paper which describes the functionality of DAN and the conventions of multiple object tracking. Thereafter, we downloaded the program from the attached GitHub repository and tested the algorithm with multiple object tracking challenges. Through testing and comparing DAN to other algorithms that do the same task we found that our model achieves the best performance in most important metrics.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:396133
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-15 09:51:07