Publication detail
Determining Vehicle Turn Counts at Multiple Intersections by Separated Vehicle Classes Using CNNs
FOLENTA, J. ŠPAŇHEL, J. BARTL, V. HEROUT, A.
Original Title
Determining Vehicle Turn Counts at Multiple Intersections by Separated Vehicle Classes Using CNNs
Type
conference paper
Language
English
Original Abstract
In our submission to the NVIDIA AI City Challenge2020, we address the problem of counting vehicles by theirclass at multiple intersections. Our solution is based oncounting by tracking principle using convolutional neuralnetworks in detection and tracking steps of the proposedmethod. We have achieved 6th place on the dataset partA of Track 1 with score S1 Total = 0.8829, (mwRMSE =4.3616, S1 Effectiveness = 0.9094, S1 Efficiency = 0.8212).
Keywords
vehicle counting, vehilce class, intersections, detection, tracking, convolutional neural networks
Authors
FOLENTA, J.; ŠPAŇHEL, J.; BARTL, V.; HEROUT, A.
Released
18. 5. 2020
Publisher
IEEE Computer Society
Location
Seattle, WA
ISBN
978-1-7281-9360-1
Book
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Edition
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISBN
2160-7516
Year of study
2020
Number
07
Pages from
2544
Pages to
2549
Pages count
6
URL
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"
}Documents