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master's thesis
Sustav za segmentaciju videozapisa
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:045845
Pugelnik, Fran
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Pugelnik, F. (2022). Sustav za segmentaciju videozapisa (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:045845

Pugelnik, Fran. "Sustav za segmentaciju videozapisa." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:045845

Pugelnik, Fran. "Sustav za segmentaciju videozapisa." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:045845

Pugelnik, F. (2022). 'Sustav za segmentaciju videozapisa', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 01 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:045845

Pugelnik F. Sustav za segmentaciju videozapisa [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 June 01] Available at: https://urn.nsk.hr/urn:nbn:hr:168:045845

F. Pugelnik, "Sustav za segmentaciju videozapisa", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:045845

Metadata
TitleSustav za segmentaciju videozapisa
Title (english)System for Segmentation of Video Sequences
AuthorFran Pugelnik
MentorTomislav Hrkać (mentor)
Committee memberTomislav Hrkać (predsjednik povjerenstva)
Committee memberSiniša Šegvić (član povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-11, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad se fokusira na pronalazak načina ubrzanja postupka segmentacije fotografije kako bi se taj postupak mogao upotrijebiti za segmentaciju videozapisa. Prvo je istražen sustav R-CNN (i njegove kasnije iteracije) za detekciju objekata, te Mask R-CNN za segmentaciju instanci. Proučivši te sustave i procijenivši njihove pozitivne aspekte i nedostatke okrenuli smo se prema načinu ubrzanja mreže. Gledajući MobileNet modele zaključeno je da bi se koncepti poput depthwise seperable konvolucije, inverzne rezidualne strukture, ekspanzijskih slojeva i „squeze and excitation“ mogli iskoristiti za ubrzanje prethodno opisanog Mask R-CNN modela. Također su proučeni benefiti jednostadijskih detektora implementiranih unutar RetinaNet-a i RetinaMaska te njihov utjecaj na performanse sustava. Na kraju je implementiran jednostavan sustav za segmentaciju objekata od pozadine, te su predložena dva sustava Efficient MaskRCNN i Efficient RetinaMask, koji bi potencijalno poboljšali performanse sustava i možda omogućili izvođenje na uređajima nižih performansi.
Abstract (english)
This paper focuses on finding ways to speed up the image segmentation process so that it can be used for video segmentation. First The R-CNN system (and its later iterations) for object detection and the Mask R-CNN for instance segmentation were investigated and by studying these systems and assessing their positive aspects and shortcomings, we turned to how to accelerate the network. Looking at MobileNet models, it was concluded that concepts such as depthwise seperable convolution, inverse residual structure, expansion layers and "squeze and excitation" could be used to accelerate the previously described Mask R-CNN model. The benefits of single-stage detectors implemented within RetinaNet and RetinaMask and their impact on system performance were also studied. Finally, a simple system for segmenting foreground form background objects was implemented and two systems, Efficient MaskRCNN and Efficient RetinaMask, were proposed which could potentially improve system performance and make the system viable on more low power devices.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:045845
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 15:57:05