Detail publikačního výsledku

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

ŠPAŇHEL, J.; SOCHOR, J.; MAKAROV, A.

Originální název

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

Anglický název

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

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.  

Anglický abstrakt

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.  

Klíčová slova

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

Klíčová slova v angličtině

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

Autoři

ŠPAŇHEL, J.; SOCHOR, J.; MAKAROV, A.

Rok RIV

2019

Vydáno

29.10.2018

Nakladatel

IEEE Signal Processing Society

Místo

Belgrade

ISBN

978-1-5386-6974-7

Kniha

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

Strany od

1

Strany do

6

Strany počet

6

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"
}