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master's thesis
Praćenje više gibajućih objekata pomoću mješavina Gaussovih razdioba
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:467484
Arambašić, Nikola
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Arambašić, N. (2022). Praćenje više gibajućih objekata pomoću mješavina Gaussovih razdioba (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:467484

Arambašić, Nikola. "Praćenje više gibajućih objekata pomoću mješavina Gaussovih razdioba." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:467484

Arambašić, Nikola. "Praćenje više gibajućih objekata pomoću mješavina Gaussovih razdioba." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:467484

Arambašić, N. (2022). 'Praćenje više gibajućih objekata pomoću mješavina Gaussovih razdioba', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 12 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:467484

Arambašić N. Praćenje više gibajućih objekata pomoću mješavina Gaussovih razdioba [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 July 12] Available at: https://urn.nsk.hr/urn:nbn:hr:168:467484

N. Arambašić, "Praćenje više gibajućih objekata pomoću mješavina Gaussovih razdioba", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:467484

Metadata
TitlePraćenje više gibajućih objekata pomoću mješavina Gaussovih razdioba
Title (english)EN: Multi-object Tracking via Gaussian Distribution Mixtures
AuthorNikola Arambašić
MentorIvan Marković (mentor)
Committee memberIvan Petrović (član povjerenstva)
Committee memberMarija Seder (član povjerenstva)
Committee memberJuraj Peršić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Praćenje više gibajućih objekata složen je problem čiji je cilj procijeniti broj objekata u određenom području zajedno s kinematičkim stanjima svakog objekta. Uz nesigurnost mjernog i procesnog modela iz klasične Bayesovske estimacije, postoji mnogo dodatnih izvora nesigurnosti u praćenju više objekata. Na primjer, broj objekata se može mijenjati s vremenom, a neki objekti mogu biti neotkriveni u nekim okvirima. Obično ne postoji ni način da se odredi koje detekcije odgovaraju kojem objektu, stoga je u takvim problemima potrebna metoda povezivanja podataka. Korištenjem teorije slučajnih konačnih skupova moguće je proširiti Bayesovu rekurziju na slučaj s više objekata. Međutim, ne postoje opća rješenja zatvorenog oblika. Ipak, postoje mnoge aproksimacije Bayesove rekurzije s više objekata temeljene na Gaussovim mješavinama. Cilj ovog diplomskog rada je implementirati algoritam praćenja više objekata Gaussove mješavine u MATLAB-u i testirati ga u simulacijama.
Abstract (english)
Multi-object tracking is a complex problem where the goal is to estimate the number of objects in the surveillance region together with the kinematic states of each object. In addition to the measurement and process model uncertainty from classical estimation, there are many additional sources of uncertainty in multi-object tracking. For example, the number of objects may change with time and some objects might be undetected in some frames. Usually, there is also no way to determine which detections correspond to which object, hence a data association method is required in such problems. By using random finite sets theory, it is possible to extend the Bayesian recursion to the multi-object case. However, there exists no general closed-form solutions. Nonetheless, there are many approximations to the multi-object Bayesian recursion based on Gaussian mixtures. The goal of this thesis is to implement a Gaussian mixture multi-object tracking algorithm in MATLAB and test it in simulations.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:467484
Study programmeTitle: Electrical Engineering and Information Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije (magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2023-01-13 11:41:32