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master's thesis
Detekcija akcija u videu korištenjem dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:942924
Matošević, Erik
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Matošević, E. (2020). Detekcija akcija u videu korištenjem dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:942924

Matošević, Erik. "Detekcija akcija u videu korištenjem dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:942924

Matošević, Erik. "Detekcija akcija u videu korištenjem dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:942924

Matošević, E. (2020). 'Detekcija akcija u videu korištenjem dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 02 October 2024, https://urn.nsk.hr/urn:nbn:hr:168:942924

Matošević E. Detekcija akcija u videu korištenjem dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 October 02] Available at: https://urn.nsk.hr/urn:nbn:hr:168:942924

E. Matošević, "Detekcija akcija u videu korištenjem dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:942924

Metadata
TitleDetekcija akcija u videu korištenjem dubokog učenja
Title (english)Action Detection in Video using Deep Learning
AuthorErik Matošević
MentorZoran Kalafatić (mentor)
Committee memberZoran Kalafatić (predsjednik povjerenstva)
Committee memberSiniša Šegvić (član povjerenstva)
Committee memberTomislav Hrkać (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-02-21, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Detekcija akcije u videozapisu složen je i izazovan zadatak s ciljem otkrivanja pojave određenih radnji u nekoj situaciji. Temelji se na korištenju dubokih neuronskih mreža koje uče model na označenim slikama od kojih se sastoji video snimka uzimajući u obzir prostornu i vremensku komponentu. U ovome radu dan je uvod u duboko učenje i neuronske mreže. Obuhvaćen je problem detekcije akcija te je opisana C3D arhitektura temeljena na konceptu 3D konvolucijske neuronske mreže koja se pokazala kao efikasno rješenje za navedeni problem. Razvijen je programski okvir za stvaranje skupa podataka te je implementirana C3D neuronska mreža, trenirana na skupu video isječaka s nogometnih utakmica čiji su rezultati opisani i prikazani.
Abstract (english)
Action detection in a video is a complex and challenging task that aims to detect the occurrences of certain actions in a situation. It is based on the use of deep neural networks that train the model on the labeled images from a video by considering the spatial and temporal component. This work provides an introduction to deep learning and neural networks. The action detection problem is covered and a C3D architecture based on the concept of a 3D convolutional neural network is described, which is proven to be an effective solution to this problem. A software framework for dataset creation was developed alongside the C3D neural network implementation. The neural network was trained on a set of video clips from football matches, and the obtained results are described and presented.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:942924
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 on2023-01-13 09:16:58