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master's thesis
Pronalaženje anomalija difuzijskim uklanjanjem šuma
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:564422
Srzić, Josip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Srzić, J. (2024). Pronalaženje anomalija difuzijskim uklanjanjem šuma (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:564422

Srzić, Josip. "Pronalaženje anomalija difuzijskim uklanjanjem šuma." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:564422

Srzić, Josip. "Pronalaženje anomalija difuzijskim uklanjanjem šuma." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:564422

Srzić, J. (2024). 'Pronalaženje anomalija difuzijskim uklanjanjem šuma', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:564422

Srzić J. Pronalaženje anomalija difuzijskim uklanjanjem šuma [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:564422

J. Srzić, "Pronalaženje anomalija difuzijskim uklanjanjem šuma", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:564422

Metadata
TitlePronalaženje anomalija difuzijskim uklanjanjem šuma
Title (english)Anomaly detection with denoising diffusion
AuthorJosip Srzić
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberAlan Jović (član povjerenstva)
Committee memberTomislav Hrkać (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-11, Croatia
Abstract
Varijacijski difuzijski modeli iskazuju impresivne performanse na zadatku detekcije anomalija. Njihova glavna mana je računalna složenost, zbog prolaska krozi dugi unatražni Markovljev proces. Procjena difuzijskog vremena (DTE) varijanta je difuzijskih modela koja ih nadmašuje u zadatku detekcije anomalija uz značajno manji broj potrebnih računalnih operacija. U ovom radu učimo model difuzijske procjene vremena na segmentima skeletona koji opisuju poze osoba u videozapisima, s ciljem pronalaženja anomalnih radnji. U osnovnom eksperimentu pokazujemo da difuzijska procjena vremena primijenjena izravno na skeletone daje kompetitivne rezultate. Zatim, segment skeletona promatramo kao prostorno-vremenski graf te u model za procjenu difuzijskog vremena ubacujemo slojeve konvolucije nad grafovima. Evaluiramo predloženu arhitekturu na skupovima UBnormal i ShanghaiTech. Naši rezultati postižu kompetitivne rezultate na skupu ShanghaiTech te nadmašuju stanje tehnike na skupu UBnormal za 4 postotna boda. Provodimo diskusiju rezultata i predlažemo pravce za budući rad.
Abstract (english)
Variational diffusion models show impressive performance on the anomaly detection task. Their main drawback is computational complexity, due to the long traversal of the backward Markov process. Diffusion Time Estimation (DTE) is a variant of diffusion models that outperforms them in the task of anomaly detection with a significantly lower number of parameters and computational power needed. In this paper, we train a diffusion time estimation model on skeleton segments describing the poses of people in videos, with the goal of detecting anomalous actions. For our baseline experiment, we show that diffusion time estimation applied directly to skeletons gives competitive results. Next, we interpret skeleton segments as spatio-temporal graphs and introduce graph convolutional layers into the diffusion time estimation model. We evaluate the proposed architecture on the UBnormal and ShanghaiTech sets. Our results achieve competitive results on ShanghaiTech and significantly surpass the state of the art on UBnormal. We perform a discussion of the results and suggest directions for future work.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:564422
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 on2024-10-12 22:09:27