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doctoral thesis
State estimation and multiple moving object tracking on Riemannian manifolds
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:573863
Bićanić, Borna
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electronics, Microelectronics, Computer and Intelligent Systems

Cite this document

Bićanić, B. (2023). State estimation and multiple moving object tracking on Riemannian manifolds (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:573863

Bićanić, Borna. "State estimation and multiple moving object tracking on Riemannian manifolds." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:573863

Bićanić, Borna. "State estimation and multiple moving object tracking on Riemannian manifolds." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:573863

Bićanić, B. (2023). 'State estimation and multiple moving object tracking on Riemannian manifolds', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 17 March 2025, https://urn.nsk.hr/urn:nbn:hr:168:573863

Bićanić B. State estimation and multiple moving object tracking on Riemannian manifolds [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2025 March 17] Available at: https://urn.nsk.hr/urn:nbn:hr:168:573863

B. Bićanić, "State estimation and multiple moving object tracking on Riemannian manifolds", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:573863

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Metadata
TitleState estimation and multiple moving object tracking on Riemannian manifolds
Title (croatian)Estimacija stanja i praćenje više gibajućih objekata na Riemannovim mnogostrukostima
AuthorBorna Bićanić
MentorIvan Petrović (mentor)
Committee memberIvan Marković (predsjednik povjerenstva)
Committee memberMario Bukal (član povjerenstva)
Committee memberRobert Cupec (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Electronics, Microelectronics, Computer and Intelligent Systems)
Zagreb
Defense date and country2023-07-17, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Universal decimal classification
(UDC)		004 - Computer science and technology. Computing. Data processing
Abstract
This thesis studies the problem of state estimation and multi-target tracking. First, the thesis presents a novel method for pedestrian tracking from the sequence of images. In visual tracking, it is also possible to obtain the appearance features by utilizing deep neural networks. However, fusing appearance cues represented by deep features of the neural networks together with the kinematic cues inside a classical multi-target tracking framework proves to be a non-trivial problem due to the nonlinearity of the underlying space of deep embeddings. To this end, this dissertation utilizes the tools of Riemannian geometry to tackle this problem. This thesis proposes a novel filtering method based on the unscented transform for systems whose state-space is a tangent bundle of a Riemannian manifold. By modeling the state-space as a tangent bundle of a manifold, it is possible to simultaneously estimate the position of the object on the manifold and its velocity, which lies in the tangent space of the manifold. In other words, it allows the use of the constant velocity motion model for state estimation on Riemannian manifolds. Although this can also be achieved with the Riemannian manifold extended Kalman filter, the proposed method avoids the tedious calculation of Jacobians. In the final part of the thesis, the state estimation methods for Riemannian manifolds are adapted to the multi-target case resulting in the novel multi-target tracking method for Riemannian manifolds based on probabilistic data association.
Abstract (croatian)
Ovaj doktorat proučava problem estimacije stanja i praćenja više objekata. Prvo, rad predstavlja novu metodu za praćenje pješaka iz niza slika. U vizualnom praćenju također je moguće dobiti značajke izgleda korištenjem dubokih neuronskih mreža. Međutim, fuzija znakova izgleda predstavljenih dubokim značajkama neuronskih mreža zajedno s kinematičkim značajkama unutar klasičnih algoritama za praćenje više objekata pokazalo se netrivijalnim problemom zbog nelinearnosti prostora dubokih značajki. U tu svrhu, ova disertacija koristi alate Riemannove geometrije za rješavanje ovog problema. Ova disertacija predlaže novu metodu filtriranja koja se temelji na transformaciji temeljenoj na sigma točkama za sustave čiji je prostor stanja tangencijalni snop Riemannove mnogostrukosti. Modeliranjem prostora stanja kao tangencijalnog snopa mnogostrukosti, moguće je istovremeno procijeniti položaj objekta na mnogostrukosti kao i njegovu brzinu, koja leži u tangencijalnom prostoru mnogostrukosti. Drugim riječima, omogućuje korištenje modela gibanja konstantne brzine za procjenu stanja na Riemannovim mnogostrukostima. Iako se to također može postići s proširenim Kalmanovim filtrom za Riemannove mnogostrukosti, predložena metoda izbjegava zamorno izračunavanje Jakobijana. U završnom dijelu rada, metode estimacije stanja za Riemannove mnogostrukosti proširene su na problem praćenja više objekata, što je rezultiralo novom metodom praćenja više objekata za Riemannove mnogostrukosti koja se temelji na vjerojatnosnoj asocijaciji podataka.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:168:573863
Promotion2023
Study programmeTitle: Computer Science
Study programme type: university
Study level: postgraduate
Academic / professional title: Doktor znanosti (Doktor znanosti)
Catalog URLhttps://lib.fer.hr/cgi-bin/koha/opac-detail.pl?biblionumber=54314
Type of resourceText
Extentxvii, 123 str.
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
RepositoryFER Repository
Created on2024-02-09 11:18:53

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