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master's thesis
Konvolucijski modeli za klasifikaciju videa
2020. urn:nbn:hr:168:751974
Borac, Antonio
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Borac, A. (2020). Konvolucijski modeli za klasifikaciju videa (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:751974

Borac, Antonio. "Konvolucijski modeli za klasifikaciju videa." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:751974

Borac, Antonio. "Konvolucijski modeli za klasifikaciju videa." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:751974

Borac, A. (2020). 'Konvolucijski modeli za klasifikaciju videa', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 05 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:751974

Borac A. Konvolucijski modeli za klasifikaciju videa [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 May 05] Available at: https://urn.nsk.hr/urn:nbn:hr:168:751974

A. Borac, "Konvolucijski modeli za klasifikaciju videa", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:751974

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Metadata
TitleKonvolucijski modeli za klasifikaciju videa
Title (english)Convolutional models for video classification
AuthorAntonio Borac
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberSlobodan Ribarić (član povjerenstva)
Committee memberVlado Glavinić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Klasifikacija videa neriješen je problem računalnog videa s mnogim zanimljivim primjenama. U posljednje vrijeme najbolji rezultati u tom području postižu se pristupima temeljenim na dubokim konvolucijskim modelima. Za ovaj rad posebno su zanimljivi nadzirani pristupi gdje je svaki video označen razredom radnje koju video predstavlja. U okviru rada proučio sam postojeće pristupe klasifikaciji videa. Objasnio sam teorijsku podlogu bitnu za razumijevanje eksperimenata koji se provode. Izgradio sam dvije konkretne arhitekture za klasifikaciju videa. Izrađena implementacija modela temelji se na radnom okviru Keras. Opisao sam i arhitekture na kojima se arhitekture od interesa baziraju. Prikazao sam i ocijenio ostvarene rezultate i predložio pravce za budući razvoj.
Abstract (english)
Video classification is an unsolved problem of computer vision with many interesting applications. Lately, best results in that area are obtained using approaches based on deep convolutional models. This thesis considers supervised approaches where every video is labeled with class of action the video represents. I examined existing approaches to video classification which were interesting for this paper. I explained the theory necessary for understanding of the experiments. Model implementation is based on Keras framework. I have built two distinct architectures for video classification. Additionally, I have explained the architectures on which the architectures of interest are based. I reported the results and compared them with results from literature and listed possible improvements and directions for future work.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:751974
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
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Created on2023-01-13 11:49:57