Result Details
Detection of Traffic Violations of Road Users Based on Convolutional Neural Networks
ŠPAŇHEL, J.; SOCHOR, J.; MAKAROV, A. 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. p. 1-6. ISBN: 978-1-5386-6974-7.
Type
conference paper
Language
English
Authors
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.
Keywords
camera calibration, convolutional neural networks, pedestrian detection, traffic violation, vehicle detection
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)
ISBN
978-1-5386-6974-7
Publisher
IEEE Signal Processing Society
Place
Belgrade
DOI
UT WoS
000457745100017
EID Scopus
BibTeX
@inproceedings{BUT155106,
author="ŠPAŇHEL, J. and SOCHOR, J. and MAKAROV, A.",
title="Detection of Traffic Violations of Road Users Based on Convolutional Neural Networks",
booktitle="2018 14th Symposium on Neural Networks and Applications (NEUREL)",
year="2018",
pages="1--6",
publisher="IEEE Signal Processing Society",
address="Belgrade",
doi="10.1109/NEUREL.2018.8586996",
isbn="978-1-5386-6974-7"
}
Projects
iARTIST - industry-Academia Research on Three-dimensional Image Sensing for Transportation, EU, Seventh Research Framework Programme, start: 2017-10-01, end: 2018-01-31, completed
Departments