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FOLENTA, J. ŠPAŇHEL, J. BARTL, V. HEROUT, A.
Originální název
Determining Vehicle Turn Counts at Multiple Intersections by Separated Vehicle Classes Using CNNs
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
In our submission to the NVIDIA AI City Challenge 2020, we address the problem of counting vehicles by their class at multiple intersections. Our solution is based on counting by tracking principle using convolutional neural networks in detection and tracking steps of the proposed method. We have achieved 6th place on the dataset part A of Track 1 with score S1 Total = 0.8829, (mwRMSE = 4.3616, S1 Effectiveness = 0.9094, S1 Efficiency = 0.8212).
Klíčová slova
vehicle counting, vehilce class, intersections, detection, tracking, convolutional neural networks
Autoři
FOLENTA, J.; ŠPAŇHEL, J.; BARTL, V.; HEROUT, A.
Vydáno
18. 5. 2020
Nakladatel
IEEE Computer Society
Místo
Seattle, WA
ISBN
978-1-7281-9360-1
Kniha
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Edice
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN
2160-7516
Ročník
2020
Číslo
07
Strany od
2544
Strany do
2549
Strany počet
6
URL
https://ieeexplore.ieee.org/document/9150881
BibTex
@inproceedings{BUT168129, author="Ján {Folenta} and Jakub {Špaňhel} and Vojtěch {Bartl} and Adam {Herout}", title="Determining Vehicle Turn Counts at Multiple Intersections by Separated Vehicle Classes Using CNNs", booktitle="2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)", year="2020", series="IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops", volume="2020", number="07", pages="2544--2549", publisher="IEEE Computer Society", address="Seattle, WA", doi="10.1109/CVPRW50498.2020.00306", isbn="978-1-7281-9360-1", issn="2160-7516", url="https://ieeexplore.ieee.org/document/9150881" }