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An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System

Video surveillance-based intrusion detection has been widely used in modern railway systems. Objects inside the alarm region, or the track area, can be detected by image processing algorithms. With the increasing number of surveillance cameras, manual labeling of alarm regions for each camera has be...

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Autores principales: Wang, Yang, Zhu, Liqiang, Yu, Zujun, Guo, Baoqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603763/
https://www.ncbi.nlm.nih.gov/pubmed/31174417
http://dx.doi.org/10.3390/s19112594
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author Wang, Yang
Zhu, Liqiang
Yu, Zujun
Guo, Baoqing
author_facet Wang, Yang
Zhu, Liqiang
Yu, Zujun
Guo, Baoqing
author_sort Wang, Yang
collection PubMed
description Video surveillance-based intrusion detection has been widely used in modern railway systems. Objects inside the alarm region, or the track area, can be detected by image processing algorithms. With the increasing number of surveillance cameras, manual labeling of alarm regions for each camera has become time-consuming and is sometimes not feasible at all, especially for pan-tilt-zoom (PTZ) cameras which may change their monitoring area at any time. To automatically label the track area for all cameras, video surveillance system requires an accurate track segmentation algorithm with small memory footprint and short inference delay. In this paper, we propose an adaptive segmentation algorithm to delineate the boundary of the track area with very light computation burden. The proposed algorithm includes three steps. Firstly, the image is segmented into fragmented regions. To reduce the redundant calculation in the evaluation of the boundary weight for generating the fragmented regions, an optimal set of Gaussian kernels with adaptive directions for each specific scene is calculated using Hough transformation. Secondly, the fragmented regions are combined into local areas by using a new clustering rule, based on the region’s boundary weight and size. Finally, a classification network is used to recognize the track area among all local areas. To achieve a fast and accurate classification, a simplified CNN network is designed by using pre-trained convolution kernels and a loss function that can enhance the diversity of the feature maps. Experimental results show that the proposed method finds an effective balance between the segmentation precision, calculation time, and hardware cost of the system.
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spelling pubmed-66037632019-07-17 An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System Wang, Yang Zhu, Liqiang Yu, Zujun Guo, Baoqing Sensors (Basel) Article Video surveillance-based intrusion detection has been widely used in modern railway systems. Objects inside the alarm region, or the track area, can be detected by image processing algorithms. With the increasing number of surveillance cameras, manual labeling of alarm regions for each camera has become time-consuming and is sometimes not feasible at all, especially for pan-tilt-zoom (PTZ) cameras which may change their monitoring area at any time. To automatically label the track area for all cameras, video surveillance system requires an accurate track segmentation algorithm with small memory footprint and short inference delay. In this paper, we propose an adaptive segmentation algorithm to delineate the boundary of the track area with very light computation burden. The proposed algorithm includes three steps. Firstly, the image is segmented into fragmented regions. To reduce the redundant calculation in the evaluation of the boundary weight for generating the fragmented regions, an optimal set of Gaussian kernels with adaptive directions for each specific scene is calculated using Hough transformation. Secondly, the fragmented regions are combined into local areas by using a new clustering rule, based on the region’s boundary weight and size. Finally, a classification network is used to recognize the track area among all local areas. To achieve a fast and accurate classification, a simplified CNN network is designed by using pre-trained convolution kernels and a loss function that can enhance the diversity of the feature maps. Experimental results show that the proposed method finds an effective balance between the segmentation precision, calculation time, and hardware cost of the system. MDPI 2019-06-06 /pmc/articles/PMC6603763/ /pubmed/31174417 http://dx.doi.org/10.3390/s19112594 Text en © 2019 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
Wang, Yang
Zhu, Liqiang
Yu, Zujun
Guo, Baoqing
An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System
title An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System
title_full An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System
title_fullStr An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System
title_full_unstemmed An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System
title_short An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System
title_sort adaptive track segmentation algorithm for a railway intrusion detection system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603763/
https://www.ncbi.nlm.nih.gov/pubmed/31174417
http://dx.doi.org/10.3390/s19112594
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