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master's thesis
Detekcija objekata u videu snimljenom iz vozila u pokretu
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:678318
Lokotar, Tomislav
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Lokotar, T. (2019). Detekcija objekata u videu snimljenom iz vozila u pokretu (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:678318

Lokotar, Tomislav. "Detekcija objekata u videu snimljenom iz vozila u pokretu." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:678318

Lokotar, Tomislav. "Detekcija objekata u videu snimljenom iz vozila u pokretu." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:678318

Lokotar, T. (2019). 'Detekcija objekata u videu snimljenom iz vozila u pokretu', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 01 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:678318

Lokotar T. Detekcija objekata u videu snimljenom iz vozila u pokretu [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 June 01] Available at: https://urn.nsk.hr/urn:nbn:hr:168:678318

T. Lokotar, "Detekcija objekata u videu snimljenom iz vozila u pokretu", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:678318

Metadata
TitleDetekcija objekata u videu snimljenom iz vozila u pokretu
Title (english)Object Detection in Video Taken from a Moving Vehicle
AuthorTomislav Lokotar
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 country2019-09-27, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Detekcija objekata u prometnim situacijama je velik izazov i ključan dio razvoja autonomnih vozila. U posljednje vrijeme, detekcija objekata vrši se gotovo isključivo dubokim učenjem. U okviru rada su proučavani modeli za detekciju objekata, pristupi njihovog učenja te detaljno opisane dvije arhitekture detektora objekata YOLO i SSD. YOLO i SSD implementirani su u nekoliko biblioteka, a proučavana implementacija je izrađena Python bibliotekom PyTorch. U eksperimentalnom dijelu, dva modela dubokog učenja su trenirani na javno dostupnom skupu podataka BDD100K s označenim objektima u prometu. Na kraju, prezentirane se su uspješnosti detektora na skupovima podataka koje modeli nisu vidjeli tokom učenja, te njihova usporedba. Evaluirane su performanse modela na hardveru modernog računala te demonstriran rad detekcijom na videu snimanom iz vozila.
Abstract (english)
Detection of objects in traffic situations is a great challenge and a key part of the development of autonomous vehicles. Lately, object detection has been done almost exclusively by deep learning. As a part of thesis, object detection models and their learning approaches were studied, with two deep learning based detectors, YOLO and SSD, described in detail. YOLO and SSD have been implemented in several libraries. The studied implementation has been created with the Python PyTorch library. In the experimental part, two deep learning models were trained on a publicly available dataset with labeled objects in BDD100K traffic. Finally, the performance of the detectors on part of dataset that the models did not see during learning are presented and a comparison is made. The performance of the models on modern computer hardware was evaluated and detection on real-life video shot in traffic conditions was demonstrated.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:678318
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 on2020-01-07 19:44:14