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Railway Station Surveillance System Design: A Real Application of an Optimal Coverage Approach

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 10242)

Abstract

The design of an effective and efficient surveillance system is fundamental for the protection of the Critical Infrastructures. In a railway station, this requirement turns on as an urgent prerequisite: for its intrinsic nature, a station represents a complex environment to be monitored for both safety and security reasons. In this work, we show how the video surveillance system of a real terminal railway station can be effectively designed in terms of sensor placement problem using an optimal coverage approach. The obtained results confirm the effectiveness of the proposed method in supporting security experts in both the design and reconfiguration of a surveillance system, in order to increase the asset security level.

Keywords

Railway security Video-surveillance Sensor placement Optimal coverage 
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References

  1. 1.
    Azim, A., Aycard, O.: Detection, classification and tracking of moving objects in a 3D environment. In: IEEE Intelligent Vehicles Symposium (IV), Alcal de Henares (2012)Google Scholar
  2. 2.
    Berman, O., Drezner, Z., Krass, D.: Generalized coverage: new developments in covering location models. Comput. Oper. Res. 37, 1675–1687 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bocchetti, C., Flammini, F., Pragliola, C., Pappalardo, A.: Dependable integrated surveillance systems for the physical security of metro railways. In: Third ACM/IEEE International Conference on IEEE Distributed Smart Cameras (ICDSC 2009), pp. 1–7 (2009)Google Scholar
  4. 4.
    Boccia, M., Sforza, A., Sterle, C.: Flow intercepting facility location: problems, models and heuristics. J. Math. Model. Algorithms 8(1), 35–79 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Caprara, A., Toth, P., Fischetti, M.: Algorithms for set covering problem. Ann. Oper. Res. 98, 353–371 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Chow, T., Cho, S.Y.: Industrial neural vision system for underground railway station platform surveillance. Adv. Eng. Inform. 16(1), 73–83 (2002)CrossRefGoogle Scholar
  7. 7.
    De Cillis, F., De Maggio, M.C., Pragliola, C., Setola, R.: Analysis of criminal and terrorist related episodes in railway infrastructure scenarios. J. Homel. Secur. Emerg. Manag. 10(2), 447–476 (2013)Google Scholar
  8. 8.
    De Cillis, F., De Maggio, M.C., Setola, R.: Vulnerability assessment in RIS scenario through a synergic use of the CPTED methodology and the system dynamics approach. In: Setola, R., Sforza, A., Vittorini, V., Pragliola, C. (eds.) Railway Infrastructure Security, Topics in Safety, Risk, Reliability and Quality, pp. 65–89. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-04426-2_4 Google Scholar
  9. 9.
    Ercan, A.O., El Gamal, A., Guibas, L.J.: Object tracking in the presence of occlusions using multiple cameras: a sensor network approach. ACM Trans. Sens. Netw. (TOSN) 9(2), 16–52 (2013)Google Scholar
  10. 10.
    Guvensan, M.A., Gokhan, Y.: On coverage issues in directional sensor networks: a survey. Ad Hoc Netw. 9(7), 1238–1255 (2011)CrossRefGoogle Scholar
  11. 11.
    Joshi, U., Patel, K.: Object tracking and classification under illumination variations. Int. J. Eng. Dev. Res. (IJEDR) 4(1), 667–670 (2016)Google Scholar
  12. 12.
    Mavrinac, A., Chen, X.: Modeling coverage in camera networks: a survey. Int. J. Comput. Vis. 101(1), 205–226 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Moon, T.H., Heo, S.Y., Leem, Y.T., Nam, K.W.: An analysis on the appropriateness and effectiveness of CCTV location for crime prevention. World Acad. Sci. Eng. Tech. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 9(3), 836–843 (2015)Google Scholar
  14. 14.
    Oh, S., Park, S., Lee, C.: A platform surveillance monitoring system using image processing for passenger safety in railway station. In: International Conference on Control, Automation and Systems, Seoul (2007)Google Scholar
  15. 15.
    Pathak, S., Aishwarya, P., Sunayana, K., Apurva, M.: Activity detection in video surveillance system. Int. J. Res. Advent Technol. 19–25 (2016). E-ISSN: 2321-9637Google Scholar
  16. 16.
    Ronetti, N., Dambra, C.: Railway station surveillance: the Italian case. In: Foresti, G.L., Mähönen, P., Regazzoni, C.S. (eds.) Multimedia Video-Based Surveillance Systems, pp. 13–20. Springer, Boston (2000).  https://doi.org/10.1007/978-1-4615-4327-5_2 CrossRefGoogle Scholar
  17. 17.
    Shi, G., Chang, L., Shuyuan, Z.: Research on passenger recognition system based on video image processing in railway passenger station. In: International Conference on Information Sciences, Machinery, Materials and Energy (ICISMME), Chongqing (2015)Google Scholar
  18. 18.
    Setola, R., Sforza, A., Vittorini, V., Pragliola, C. (eds.): Railway Infrastructure Security. Topics in Safety, Risk, Reliability and Quality. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-04426-2 Google Scholar
  19. 19.
    Sterle, C., Sforza, A., Esposito, A.A., Piccolo, C.: A unified solving approach for two and three dimensional coverage problems in sensor networks. Opt. Lett. 10, 1–23 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Sun, J., Lo, B.P.L., Velastin, S.A.: Fusing visual and audio information in a distributed intelligent surveillance system for public transport systems. Acta Autom. Sin. 20(3), 393–407 (2003)Google Scholar
  21. 21.
    Valera, M., Velastin, S.A.: Intelligent distributed surveillance systems: a review. IEEE Proc. Vis. Image Sig. Process. 152(2), 192–204 (2005)CrossRefGoogle Scholar
  22. 22.
    Xiao, Y., Farooq, A.R., Smith, M., Wright, D., Wright, G.: Robust left object detection and verification in video surveillance. In: IAPR International Conference on Machine Vision Applications, MVA 2013, Kyoto (2013)Google Scholar
  23. 23.
    Wang, B.: Coverage problems in sensor networks: a survey. ACM Comput. Surv. 43(4), 32–43 (2011)CrossRefGoogle Scholar
  24. 24.
    Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recogn. Lett. 34(1), 3–19 (2013)CrossRefGoogle Scholar
  25. 25.
    Zhang, R., Ding, J.: Object tracking and detecting based on adaptive background subtraction. Procedia Eng. 29, 1351–1355 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Complex Systems and Security LaboratoryUniversity Campus Bio-Medico of RomeRomeItaly
  2. 2.Security Department - Technical AreaRete Ferroviaria ItalianaRomeItaly
  3. 3.Security Department - DirectorateFerrovie dello Stato ItalianeRomeItaly
  4. 4.Department of Electrical Engineering and Information TechnologyUniversity Federico II of NaplesNaplesItaly

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