Intrusion Detection at Railway Tunnel Entrances Using Dynamic Vision Sensors

Colin Gebler, Regina Pohle-Fröhlich

2024

Abstract

The surveillance of railway tunnel entrances is integral to ensure the security of both people and infrastructure. Since 24/7 personal surveillance is not economically possible, it falls to automated solutions to ensure that no persons can intrude unseen. We investigate the use of Dynamic Vision Sensors in fulfilling this task. A Dynamic Vision Sensor differs from a traditional frame-based camera in that it does not record entire images at a fixed rate. Instead, each pixel outputs events independently and asynchronously whenever a change in brightness occurs at that location. We present a dataset recorded over three months at a railway tunnel entrance, with relevant examples assigned labeled as featuring or not featuring intrusions. Furthermore, we investigate intrusion detection by using neural networks to perform image classification on images generated from the event stream using established methods to represent the temporal information in that format. Of the models tested, MobileNetV2 achieved the best result with a classification accuracy of 99 .55% on our dataset when differentiating between Event Volumes that do or do not contain people.

Paper Citation

in Harvard Style

Gebler C. and Pohle-Fröhlich R. (2024). Intrusion Detection at Railway Tunnel Entrances Using Dynamic Vision Sensors. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 902-909. DOI: 10.5220/0012558600003654

in Bibtex Style

@conference{icpram24,
author={Colin Gebler and Regina Pohle-Fröhlich},
title={Intrusion Detection at Railway Tunnel Entrances Using Dynamic Vision Sensors},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={902-909},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012558600003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Intrusion Detection at Railway Tunnel Entrances Using Dynamic Vision Sensors
SN - 978-989-758-684-2
AU - Gebler C.
AU - Pohle-Fröhlich R.
PY - 2024
SP - 902
EP - 909
DO - 10.5220/0012558600003654
PB - SciTePress