Skip to main content

A Methodology for Defining Smart Camera Surveillance Locations in Urban Settings

  • Conference paper
  • First Online:
  • 1345 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11621))

Abstract

In this paper we propose a methodology to solve the problem of locating a set of cameras in an uncontrolled open space, such as a city. For this purpose, the geometric approach of the problem is transformed towards the optimization of a surveillance service system in which a metaheuristic model is used to maximize the service capabilities of the set of cameras.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bennett, T., Gelsthorpe, L.: Public attitudes towards CCTV in public places. Stud. Crime Crime Prev. 5(1), 72–90 (1996)

    Google Scholar 

  2. Waples, S., Gill, M., Fisher, P.: Does CCTV displace crime? Criminol. Crim. Justice 9(2), 207–224 (2009)

    Article  Google Scholar 

  3. Li, A.: Pros and Cons of Surveillance Cameras in Public Places (2017). https://reolink.com/pros-cons-of-surveillance-cameras-in-public-places

  4. Bowcott, O.: CCTV boom has failed to slash crime, say police (2008). https://www.theguardian.com/uk/2008/may/06/ukcrime1

  5. Norris, C., McCahill, M., Wood, D.: The growth of CCTV: a global perspective on the international diffusion of video surveillance in publicly accessible space. Surveill. Soc. 2(2–3), 110–135 (2004). Editorial

    Google Scholar 

  6. Kelly, H.: After Boston: The pros and cons of surveillance cameras (2013). https://edition.cnn.com/2013/04/26/tech/innovation/security-cameras-boston-bombings/index.html

  7. Bodor, R., Schrater, P., Papanikolopoulos, N.: Multi-camera positioning to optimize task observability. In: IEEE International Conference on Advanced Video And Signal Based Surveillance - Proceedings of AVSS 2005 (2005). https://doi.org/10.1109/AVSS.2005.1577328

  8. Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. Part C 34(3), 334–352 (2004). https://doi.org/10.1016/j.artint.2008.12.005

    Article  Google Scholar 

  9. Jun, S., Chang, T., Yoon, H.: Placing visual sensors using heuristic algorithms for bridge surveillance. Appl. Sci. 8(1) (2018). https://doi.org/10.3390/app8010070

    Article  Google Scholar 

  10. Morris, B.T., Trivedi, M.M.: A survey of vision-based trajectory learning and analysis for surveillance. IEEE Trans. Circ. Syst. Video Technol. 18(8), 1114–1127 (2008). https://doi.org/10.1109/TCSVT.2008.927109

    Article  Google Scholar 

  11. Hogan, K., ReVelle, C.: Concepts and applications of backup coverage. Manag. Sci. 32(11), 1290–1306 (2012)

    Google Scholar 

  12. Rana, S.: Isovist Analyst - An Arcview extension for planning visual surveillance. ESRI International User Conference. ESRI (on CD-ROM), 1(Chvátal), 9 (2006). http://eprints.ucl.ac.uk/2104

  13. Basu, S., Sharma, M., Ghosh, P.S.: Metaheuristic applications on discrete facility location problems: a survey. OPSEARCH 52, 530 (2015). https://doi.org/10.1007/s12597-014-0190-5

    Article  MathSciNet  MATH  Google Scholar 

  14. Jordanski, M.: Metaheuristic approaches for solving facility location and scale decision problem with customer preference. IPSI BgD Trans. (Two Res. Oriented J.) 13(1) (2017). http://ipsitransactions.org/journals/papers/tir/2017jan/p2.pdf

  15. Xie, Y., Wang, M., Liu, X., Wu, Y.: Surveillance video synopsis in GIS. ISPRS Int. J. Geo-Inf. (2017). https://doi.org/10.3390/ijgi6110333

    Article  Google Scholar 

  16. Konda, K.R., Conci, N.: Global and local coverage maximization in multi-camera networks by stochastic optimization. Infocommun. J. (2013). https://doi.org/10.1200/jco.2011.35.9182

    Article  Google Scholar 

  17. Xu, Y.C., Lei, B., Hendriks, E.A.: Camera network coverage improving by particle swarm optimization. EURASIP J. Image Video Process. (2011). https://doi.org/10.1155/2011/458283

    Article  Google Scholar 

  18. O’Rourke, J.: Art Gallery Theorems and Algorithms. Oxford University Press, Oxford (1987)

    MATH  Google Scholar 

  19. Church, R., Meadows, M.: Location modeling utilizing maximum service distance criteria. Geogr. Anal. 11(4), 358–373 (1979)

    Article  Google Scholar 

  20. Murray, A., Kim, K., Davis, J., Machiraju, R., Parent, R.: Coverage optimization to support security monitoring. Comput. Environ. Urban Syst. (2007). https://doi.org/10.1016/j.compenvurbsys.2006.06.002

    Article  Google Scholar 

  21. Giagkiozis, I., Purshouse, R., Fleming, P.: An overview of population-based algorithms for multi-objective optimization. Int. J. Syst. Sci. 46(9), 1572–1599 (2015). https://doi.org/10.1080/00207721.2013.823526

    Article  MATH  Google Scholar 

  22. Tong, D., Murray, A.: Spatial optimization in geography. Ann. Assoc. Am. Geogr. 102(6), 1434–1444 (1986)

    Google Scholar 

  23. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341 (1997). https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  24. Li, X., Yin, M.: Application of differential evolution algorithm on self-potential data. PLoS ONE 7(12), e51199 (2012). https://doi.org/10.1371/journal.pone.0051199

    Article  Google Scholar 

  25. Datos de Afluencia. Patronato de la Feria Nacional de San Marcos - Coordinación Estatal de Planeación y Proyectos. http://www.aguascalientes.gob.mx/ceplap/datos/default.aspx

  26. Historia de la Feria Nacional de San Marcos en Aguascalientes: México Desconocido, 31 March 2016. https://www.mexicodesconocido.com.mx/feria-san-marcos-aguascalientes.html

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rodrigo Tapia-McClung .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tapia-McClung, R., Gómez-Fernández, T. (2019). A Methodology for Defining Smart Camera Surveillance Locations in Urban Settings. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24302-9_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24301-2

  • Online ISBN: 978-3-030-24302-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics