Skip to main content
main-content
Top

Hint

Swipe to navigate through the chapters of this book

2017 | OriginalPaper | Chapter

Large Scale Surveillance, Detection and Alerts Information Management System for Critical Infrastructure

Authors: Z. Sabeur, Z. Zlatev, P. Melas, G. Veres, B. Arbab-Zavar, L. Middleton, N. Museux

Published in: Environmental Software Systems. Computer Science for Environmental Protection

Publisher: Springer International Publishing

share
SHARE

Abstract

A proof-of-concept system for large scale surveillance, detection and alerts information management (SDAIM) is presented in this paper. Various aspects of building the SDAIM software system for large scale critical infrastructure monitoring and decision support are described. The work is currently developped in the large collaborative ZONeSEC project (www.​zonesec.​eu). ZONeSEC specializes in the monitoring of so-called Wide-zones. These are large critical infrastructure which require 24/7 monitoring for safety and security. It involves integrated in situ and remote sensing together with large scale stationary sensor networks, that are supported by cross-border communication. In ZONeSEC, the specific deployed sensors around the critical infrastructure may include: Accelerometers that are mounted on perimeter fences; Underground acoustic sensors; Optical, thermal and hyperspectral video cameras or radar systems mounted on strategic areas or on airborne UAVs for mission exploration. The SDAIM system design supports the ingestion of the various types of sensors platform wide-zones’ environmental observations and provide large scale distributed data fusion and reasoning with near-real-time messaging and alerts for critical decision-support. On a functional level, the system design is founded on the JDL/DFIG (Joint Directors of Laboratories/Data Fusion Information Group) data and information fusion model. Further, it is technologically underpinned by proven Big Data technologies for distributed data storage and processing as well as on-demand access to intelligent data analytics modules. The SDAIM system development will be piloted and alidated at various selected ZONeSEC project wide-zones [1]. These include water, oil and transnational gas pipelines and motorway conveyed in six European countries.

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 15 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 15 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 15 Tage kostenlos.

Footnotes
1
At this stage, only the first four levels of the JDL/DFIG framework are described. The full extended level will be described in the full paper as defined in Fig. 1.
 
Literature
2.
go back to reference Lambert, D.: A blueprint for higher level fusion systems. Inf. Fusion 10(1), 6–24 (2009) CrossRef Lambert, D.: A blueprint for higher level fusion systems. Inf. Fusion 10(1), 6–24 (2009) CrossRef
3.
go back to reference Sabeur, Z.: Structured multi-level data fusion and modelling of heterogeneous environmental data for future internet applications. In: Geophysical Research Abstracts. EGU General Assembly 2013, vol. 15 (2013) Sabeur, Z.: Structured multi-level data fusion and modelling of heterogeneous environmental data for future internet applications. In: Geophysical Research Abstracts. EGU General Assembly 2013, vol. 15 (2013)
4.
go back to reference Zlatev, Z., Veres, G., Sabeur, Z.: Agile data fusion and knowledge base architecture for critical decision support. Int. J. Decision Support Syst. Technol. (IJDSST) 5(2), 1–20 (2013) CrossRef Zlatev, Z., Veres, G., Sabeur, Z.: Agile data fusion and knowledge base architecture for critical decision support. Int. J. Decision Support Syst. Technol. (IJDSST) 5(2), 1–20 (2013) CrossRef
5.
go back to reference Barat, V., Grishin, D., Rostovtsev, M.: Detection of AE signals against background friction. J. Acoust. Emission 29, 133–141 (2011) Barat, V., Grishin, D., Rostovtsev, M.: Detection of AE signals against background friction. J. Acoust. Emission 29, 133–141 (2011)
6.
go back to reference Yousefi, A., Dibazar, A., Berger, T.: Application of non-homogeneous HMM on detecting security fence breaching. In: Proceedings of the ICASSP (2010) Yousefi, A., Dibazar, A., Berger, T.: Application of non-homogeneous HMM on detecting security fence breaching. In: Proceedings of the ICASSP (2010)
8.
go back to reference Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. PAMI 30(3), 555–560 (2008) CrossRef Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. PAMI 30(3), 555–560 (2008) CrossRef
9.
go back to reference Breitenstein, M.D., Grabner, H., Van Gool, L.: Hunting nessie-real-time abnormality detection from webcams. In: IEEE 12th International Conference on Computer Vision (ICCV) Workshops (2009) Breitenstein, M.D., Grabner, H., Van Gool, L.: Hunting nessie-real-time abnormality detection from webcams. In: IEEE 12th International Conference on Computer Vision (ICCV) Workshops (2009)
10.
go back to reference Saligrama, V., Chen, Z.: Video anomaly detection based on local statistical aggregates. In: Computer Vision and Pattern Recognition (CVPR) (2012) Saligrama, V., Chen, Z.: Video anomaly detection based on local statistical aggregates. In: Computer Vision and Pattern Recognition (CVPR) (2012)
11.
go back to reference Yun, K., Kim, J., Kim, S.W., Jeong, H., Choi, J.Y.: Learning with adaptive rate for online detection of unusual appearance. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., McMahan, R., Jerald, J., Zhang, H., Drucker, Steven M., Kambhamettu, C., El Choubassi, M., Deng, Z., Carlson, M. (eds.) ISVC 2014. LNCS, vol. 8887, pp. 698–707. Springer, Cham (2014). https://​doi.​org/​10.​1007/​978-3-319-14249-4_​67 CrossRef Yun, K., Kim, J., Kim, S.W., Jeong, H., Choi, J.Y.: Learning with adaptive rate for online detection of unusual appearance. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., McMahan, R., Jerald, J., Zhang, H., Drucker, Steven M., Kambhamettu, C., El Choubassi, M., Deng, Z., Carlson, M. (eds.) ISVC 2014. LNCS, vol. 8887, pp. 698–707. Springer, Cham (2014). https://​doi.​org/​10.​1007/​978-3-319-14249-4_​67 CrossRef
12.
go back to reference Shah, M., Javed, O., Shafique, K.: Automated visual surveillance in realistic scenarios. IEEE MultiMed. 14(1), 30–39 (2007) CrossRef Shah, M., Javed, O., Shafique, K.: Automated visual surveillance in realistic scenarios. IEEE MultiMed. 14(1), 30–39 (2007) CrossRef
13.
go back to reference Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Vision and Pattern Recognition (2005) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Vision and Pattern Recognition (2005)
14.
go back to reference Bouchrika, I., Carter, J.N., Nixon, M.S., Morzinger, R., Thallinger, G.: Using gait features for improving walking people detection. In: International Conference on Pattern Recognition (2010) Bouchrika, I., Carter, J.N., Nixon, M.S., Morzinger, R., Thallinger, G.: Using gait features for improving walking people detection. In: International Conference on Pattern Recognition (2010)
15.
go back to reference Niebles, J.C., Han, B., Fei-Fei, L.: Efficient extraction of human motion volumes by tracking. In: IEEE Computer Vision and Pattern Recognition (2010) Niebles, J.C., Han, B., Fei-Fei, L.: Efficient extraction of human motion volumes by tracking. In: IEEE Computer Vision and Pattern Recognition (2010)
16.
go back to reference Chaquet, J.M., Carmona, E.J., Fernández-Caballero, A.: A survey of video datasets for human action and activity recognition. Comput. Vis. Image Underst. 117(6), 633–659 (2013) CrossRef Chaquet, J.M., Carmona, E.J., Fernández-Caballero, A.: A survey of video datasets for human action and activity recognition. Comput. Vis. Image Underst. 117(6), 633–659 (2013) CrossRef
17.
go back to reference Joo, S., Zheng, Q.: A temporal variance-based moving target detector. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) (2005) Joo, S., Zheng, Q.: A temporal variance-based moving target detector. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) (2005)
Metadata
Title
Large Scale Surveillance, Detection and Alerts Information Management System for Critical Infrastructure
Authors
Z. Sabeur
Z. Zlatev
P. Melas
G. Veres
B. Arbab-Zavar
L. Middleton
N. Museux
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-89935-0_20

Premium Partner