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Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation

Anomaly detection in sequences is a complex problem in security and surveillance. With the exponential growth of surveillance cameras in urban roads, automating them to analyze the data and automatically identify anomalous events efficiently is essential. This paper presents a methodology to detect...

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Autores principales: García-Aguilar, Iván, Luque-Baena, Rafael Marcos, Domínguez, Enrique, López-Rubio, Ezequiel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458462/
https://www.ncbi.nlm.nih.gov/pubmed/37631721
http://dx.doi.org/10.3390/s23167185
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author García-Aguilar, Iván
Luque-Baena, Rafael Marcos
Domínguez, Enrique
López-Rubio, Ezequiel
author_facet García-Aguilar, Iván
Luque-Baena, Rafael Marcos
Domínguez, Enrique
López-Rubio, Ezequiel
author_sort García-Aguilar, Iván
collection PubMed
description Anomaly detection in sequences is a complex problem in security and surveillance. With the exponential growth of surveillance cameras in urban roads, automating them to analyze the data and automatically identify anomalous events efficiently is essential. This paper presents a methodology to detect anomalous events in urban sequences using pre-trained convolutional neural networks (CNN) and super-resolution (SR) models. The proposal is composed of two parts. In the offline stage, the pre-trained CNN model evaluated a large dataset of urban sequences to detect and establish the common locations of the elements of interest. Analyzing the offline sequences, a density matrix is calculated to learn the spatial patterns and identify the most frequent locations of these elements. Based on probabilities previously calculated from the offline analysis, the pre-trained CNN, now in an online stage, assesses the probability of anomalies appearing in the real-time sequence using the density matrix. Experimental results demonstrate the effectiveness of the presented approach in detecting several anomalies, such as unusual pedestrian routes. This research contributes to urban surveillance by providing a practical and reliable method to improve public safety in urban environments. The proposed methodology can assist city management authorities in proactively detecting anomalies, thus enabling timely reaction and improving urban safety.
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spelling pubmed-104584622023-08-27 Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation García-Aguilar, Iván Luque-Baena, Rafael Marcos Domínguez, Enrique López-Rubio, Ezequiel Sensors (Basel) Article Anomaly detection in sequences is a complex problem in security and surveillance. With the exponential growth of surveillance cameras in urban roads, automating them to analyze the data and automatically identify anomalous events efficiently is essential. This paper presents a methodology to detect anomalous events in urban sequences using pre-trained convolutional neural networks (CNN) and super-resolution (SR) models. The proposal is composed of two parts. In the offline stage, the pre-trained CNN model evaluated a large dataset of urban sequences to detect and establish the common locations of the elements of interest. Analyzing the offline sequences, a density matrix is calculated to learn the spatial patterns and identify the most frequent locations of these elements. Based on probabilities previously calculated from the offline analysis, the pre-trained CNN, now in an online stage, assesses the probability of anomalies appearing in the real-time sequence using the density matrix. Experimental results demonstrate the effectiveness of the presented approach in detecting several anomalies, such as unusual pedestrian routes. This research contributes to urban surveillance by providing a practical and reliable method to improve public safety in urban environments. The proposed methodology can assist city management authorities in proactively detecting anomalies, thus enabling timely reaction and improving urban safety. MDPI 2023-08-15 /pmc/articles/PMC10458462/ /pubmed/37631721 http://dx.doi.org/10.3390/s23167185 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
García-Aguilar, Iván
Luque-Baena, Rafael Marcos
Domínguez, Enrique
López-Rubio, Ezequiel
Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation
title Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation
title_full Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation
title_fullStr Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation
title_full_unstemmed Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation
title_short Small-Scale Urban Object Anomaly Detection Using Convolutional Neural Networks with Probability Estimation
title_sort small-scale urban object anomaly detection using convolutional neural networks with probability estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458462/
https://www.ncbi.nlm.nih.gov/pubmed/37631721
http://dx.doi.org/10.3390/s23167185
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