Artificial Intelligence-based Surveillance System for Railway Crossing Traffic
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU137506" target="_blank" >RIV/00216305:26220/20:PU137506 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9226453" target="_blank" >https://ieeexplore.ieee.org/document/9226453</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JSEN.2020.3031861" target="_blank" >10.1109/JSEN.2020.3031861</a>
Alternative languages
Result language
angličtina
Original language name
Artificial Intelligence-based Surveillance System for Railway Crossing Traffic
Original language description
The application of Artificial Intelligence (AI) based techniques has strong potential to improve safety and efficiency in data-driven Intelligent Transportation Systems (ITS) as well as in the emerging Internet of Vehicles (IoV) services. This paper deals with the practical implementation of deep learning methods for increasing safety and security in a specific ITS scenario: railway crossings. This research work presents our proposed system called Artificial Intelligence-based Surveillance System for Railway Crossing Traffic (AISS4RCT) that is based on a combination of detection and classification methods focusing on various image processing inputs: vehicle presence, pedestrian presence, vehicle trajectory tracking, railway barriers at railway crossings, railway warnings, and light signaling systems. The designed system uses cameras that are suitably positioned to capture an entire crossing area at a given railway crossing. By employing GPU accelerated image processing techniques and deep neural networks, the system autonomously detects risky and dangerous situations at railway crossing in real-time. In addition, camera modules send data to a central server for further processing as well as notification to interested parties (police, emergency services, railway operators). Furthermore, the system architecture employs privacy-by-design and security-by-design best practices in order to secure all communication interfaces, protect personal data, and to increase personal privacy, i.e., pedestrians, drivers. Finally, we present field-based results of detection methods, and using the YOLO tiny model method we achieve average recall 89%. The results indicate that our system is efficient for evaluating the occurrence of objects and situations, and it’s practicality for use in railway crossings.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
<a href="/en/project/FV40372" target="_blank" >FV40372: Autonomous system for detecting dangerous traffic situations based on image sequence analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE SENSORS JOURNAL
ISSN
1530-437X
e-ISSN
1558-1748
Volume of the periodical
neuveden
Issue of the periodical within the volume
2020
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
15515-15526
UT code for WoS article
000673632700099
EID of the result in the Scopus database
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