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Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will...

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Autores principales: Tsiktsiris, Dimitris, Dimitriou, Nikolaos, Lalas, Antonios, Dasygenis, Minas, Votis, Konstantinos, Tzovaras, Dimitrios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506808/
https://www.ncbi.nlm.nih.gov/pubmed/32882846
http://dx.doi.org/10.3390/s20174943
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author Tsiktsiris, Dimitris
Dimitriou, Nikolaos
Lalas, Antonios
Dasygenis, Minas
Votis, Konstantinos
Tzovaras, Dimitrios
author_facet Tsiktsiris, Dimitris
Dimitriou, Nikolaos
Lalas, Antonios
Dasygenis, Minas
Votis, Konstantinos
Tzovaras, Dimitrios
author_sort Tsiktsiris, Dimitris
collection PubMed
description Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions.
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spelling pubmed-75068082020-09-26 Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures Tsiktsiris, Dimitris Dimitriou, Nikolaos Lalas, Antonios Dasygenis, Minas Votis, Konstantinos Tzovaras, Dimitrios Sensors (Basel) Article Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions. MDPI 2020-09-01 /pmc/articles/PMC7506808/ /pubmed/32882846 http://dx.doi.org/10.3390/s20174943 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tsiktsiris, Dimitris
Dimitriou, Nikolaos
Lalas, Antonios
Dasygenis, Minas
Votis, Konstantinos
Tzovaras, Dimitrios
Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures
title Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures
title_full Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures
title_fullStr Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures
title_full_unstemmed Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures
title_short Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures
title_sort real-time abnormal event detection for enhanced security in autonomous shuttles mobility infrastructures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506808/
https://www.ncbi.nlm.nih.gov/pubmed/32882846
http://dx.doi.org/10.3390/s20174943
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