Thesis Details

Počítání vozidel ve statickém obraze

Bachelor's Thesis Student: Jelínek Zdeněk Academic Year: 2019/2020 Supervisor: Špaňhel Jakub, Ing.
English title
Vehicle Counting in Still Image
Language
Czech
Abstract

The main goal of this thesis was to compare different approaches to vehicle counting by density estimation. Four convolutional neural networks were tested - Counting CNN, Hydra CNN, Perspective-Aware CNN and Multi-column CNN. The evaluation of these models was done on three different datasets. The Perspective-aware CNN has achieved the most accurate results across all datasets. This model has reached 2.86 Mean Absolute Error on the PUCPR+ dataset, proving that it is the most suitable for the vehicle counting problem.

Keywords

convolutional neural networks, density map, density estimation, vehicle counting, Counting CNN, Hydra CNN, Perspective-Aware CNN, Multi-column CNN

Department
Degree Programme
Information Technology
Files
Status
defended, grade A
Date
8 July 2020
Reviewer
Committee
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT), předseda
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Burget Radek, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Citation
JELÍNEK, Zdeněk. Počítání vozidel ve statickém obraze. Brno, 2020. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2020-07-08. Supervised by Špaňhel Jakub. Available from: https://www.fit.vut.cz/study/thesis/23070/
BibTeX
@bachelorsthesis{FITBT23070,
    author = "Zden\v{e}k Jel\'{i}nek",
    type = "Bachelor's thesis",
    title = "Po\v{c}\'{i}t\'{a}n\'{i} vozidel ve statick\'{e}m obraze",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2020,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/23070/"
}
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