Publication Details

Detection of Traffic Violations of Road Users Based on Convolutional Neural Networks

ŠPAŇHEL Jakub, SOCHOR Jakub and MAKAROV Aleksej. Detection of Traffic Violations of Road Users Based on Convolutional Neural Networks. In: 2018 14th Symposium on Neural Networks and Applications (NEUREL). Belgrade: IEEE Signal Processing Society, 2018, pp. 1-6. ISBN 978-1-5386-6974-7.

Czech title

Detekce dopravních přestupků uživatelů pozemních komunikací s pomocí neuronových sítí

Type

conference paper

Language

english

Authors

Špaňhel Jakub, Ing., Ph.D. (DCGM FIT BUT)
Sochor Jakub, Ing. (DCGM FIT BUT)
Makarov Aleksej (Vlatacom d.o.o.)

Keywords

camera calibration, convolutional neural networks, pedestrian detection, traffic violation, vehicle detection

Abstract

In this paper, we explore the implementation
of vehicle and pedestrian detection based on neural networks
in a real-world application. We suggest changes to the
previously published method with respect to capabilities of
low-powered devices, such as Nvidia Jetson platform. Our
experimental evaluation shows that detectors are capable of
running 10.7 FPS on Jetson TX2 and can be used in real-world applications.

Published

2018

Pages

1-6

Proceedings

2018 14th Symposium on Neural Networks and Applications (NEUREL)

Conference

2018 14th Symposium on Neural Networks and Applications (NEUREL), SAVA Center Milentija Popovića 9 11070, Belgrade, Serbia, RS

ISBN

978-1-5386-6974-7

Publisher

IEEE Signal Processing Society

Place

Belgrade, RS

DOI

10.1109/NEUREL.2018.8586996

UT WoS

000457745100017

EID Scopus

2-s2.0-85060927923

BibTeX

@INPROCEEDINGS{FITPUB11850,
   author = "Jakub \v{S}pa\v{n}hel and Jakub Sochor and Aleksej Makarov",
   title = "Detection of Traffic Violations of Road Users Based on Convolutional Neural Networks",
   pages = "1--6",
   booktitle = "2018 14th Symposium on Neural Networks and Applications (NEUREL)",
   year = 2018,
   location = "Belgrade, RS",
   publisher = "IEEE Signal Processing Society",
   ISBN = "978-1-5386-6974-7",
   doi = "10.1109/NEUREL.2018.8586996",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11850"
}