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Article

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

1
The University of Sheffield, Sheffield S1 3JD, UK
2
Network Rail, Milton Keynes MK9 1EN, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Enrique Domínguez and Rafael M. Luque-Baena
Sensors 2022, 22(12), 4324; https://doi.org/10.3390/s22124324
Received: 8 April 2022 / Revised: 22 May 2022 / Accepted: 26 May 2022 / Published: 7 June 2022
(This article belongs to the Special Issue Unusual Behavior Detection Based on Machine Learning)
Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered. View Full-Text
Keywords: surveillance; rail network systems; image and video analytics; computer vision; machine learning; sensors; video anomaly detection surveillance; rail network systems; image and video analytics; computer vision; machine learning; sensors; video anomaly detection
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MDPI and ACS Style

Zhang, T.; Aftab, W.; Mihaylova, L.; Langran-Wheeler, C.; Rigby, S.; Fletcher, D.; Maddock, S.; Bosworth, G. Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey. Sensors 2022, 22, 4324. https://doi.org/10.3390/s22124324

AMA Style

Zhang T, Aftab W, Mihaylova L, Langran-Wheeler C, Rigby S, Fletcher D, Maddock S, Bosworth G. Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey. Sensors. 2022; 22(12):4324. https://doi.org/10.3390/s22124324

Chicago/Turabian Style

Zhang, Tianhao, Waqas Aftab, Lyudmila Mihaylova, Christian Langran-Wheeler, Samuel Rigby, David Fletcher, Steve Maddock, and Garry Bosworth. 2022. "Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey" Sensors 22, no. 12: 4324. https://doi.org/10.3390/s22124324

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