Thesis Details

Počítání vozidel ve statickém obraze

Bachelor's Thesis Student: Vágner Filip Academic Year: 2021/2022 Supervisor: Špaňhel Jakub, Ing.
English title
Vehicle Counting in Still Image
Language
Czech
Abstract

The goal of this work is to compare models of convolutional neural networks designed to count vehicles in a static image using density estimation with a focus on different sizes of objects in the scene. A total of four models were evaluated - Scale Pyramid Network, Scale-adaptive CNN, Multi-scale fusion network and CASA-Crowd. The evaluation was done on three data sets - TRANCOS, CARPK, PUCPR+. Scale Pyramid Network achieved the best results. The model reached 5.44 in the Mean Absolute Error metric and 9.95 in the GAME(3) metric on TRANCOS dataset.

Keywords

convolutional neural networks, vehicle counting, density estimation, density map, Scale Pyramid Network, Scale-adaptive CNN, Multi-scale fusion network, CASA-Crowd

Department
Degree Programme
Files
Status
defended, grade C
Date
14 June 2022
Reviewer
Committee
Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT), předseda
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Martínek Tomáš, Ing., Ph.D. (DCSY FIT BUT), člen
Matoušek Petr, doc. Ing., Ph.D., M.A. (DIFS FIT BUT), člen
Citation
VÁGNER, Filip. Počítání vozidel ve statickém obraze. Brno, 2022. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2022-06-14. Supervised by Špaňhel Jakub. Available from: https://www.fit.vut.cz/study/thesis/24952/
BibTeX
@bachelorsthesis{FITBT24952,
    author = "Filip V\'{a}gner",
    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 = 2022,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/24952/"
}
Back to top