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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 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).
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
2160-7516
Year of study
2020
Number
07
Pages from
2544
Pages to
2549
Pages count
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" }