Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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A Genetic Algorithm to Solve the Optimum Location Problem for Surveillance Sensors

  • Kim, NamHoon (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Sang-Pil (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Mi-Kyeong (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Sohn, Hong-Gyoo (Department of Civil and Environmental Engineering, Yonsei University)
  • Received : 2016.07.29
  • Accepted : 2016.10.11
  • Published : 2016.12.31
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Abstract

Due to threats caused by social disasters, operating surveillance devices are essential for social safety. CCTV, infrared cameras and other surveillance equipment are used to observe threats. This research proposes a method for searching for the optimum location of surveillance sensors. A GA (Genetic Algorithm) was used, since this algorithm is one of the most reasonable and efficient methods for solving complex non-linear problems. The sensor specifications, a DEM (Digital Elevation Model) and VITD (Vector Product Interim Terrain Data) maps were used for input data. We designed a chromosome using the sensor pixel location, and used elitism selection and uniform crossover for searching final solution. A fitness function was derived by the number of detected pixels on the borderline and the sum of the detection probability in the surveillance zone. The results of a 5-sensor and a 10-sensor were compared and analyzed.

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References

  1. Bang, S., Heo, J., Han, S., and Sohn, H. G. (2010), Infiltration route analysis using thermal observation devices (TOD) and optimization techniques in a GIS environment, Sensors, Vol. 10, No. 1, pp. 342-360. https://doi.org/10.3390/s100100342
  2. Cooper, L. (1964), Heuristic methods for location-allocation problems, Siam Review, Vol. 6, No. 1, pp. 37-53. https://doi.org/10.1137/1006005
  3. Eo, Y.D., Park, W.Y., Lee, Y.W., Lee, B.K., and Pyeon, M.W. (2008), The effect of digital elevation resolution on LOS analysis, Journal of the Korea Institute of Military Science and Technology, Vol. 11, No. 3, pp. 99-105. (in Korean with English abstract)
  4. Holland, J. H. (1992), Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. U Michigan Press, Ann Arbor, MI
  5. Institute for Economics & Peace (2015), Global Terrorism Index, IEP Report 36, Institute for Economics & Peace, Sydney, pp. 14.
  6. Kong, S.P., Song, H.S., Eo, Y.D., Kim, Y.M., and Kim, C.J. (2012), LOS analysis simulation considering canopy cover, Journal of the Korean Society for Geospatial Information System, Vol. 20, No. 2, pp. 55-61. (in Korean with English abstract) https://doi.org/10.7319/kogsis.2012.20.2.055
  7. Lee, Y.W., Sung, C.S., Yang, W.S., Im, S.B., and Eo, Y.D. (2006), Experimental research on the optimal surveillance equipment allocation using geo-spatial information, Journal of the Korea Institute of Military Science and Technology, Vol. 9, No. 1, pp. 72-79. (in Korean with English abstract)
  8. Mittal, A. and Davis, L. S. (2004), Visibility analysis and sensor planning in dynamic environments, European Conference on Computer Vision, 11-14 May, Prague, CZE, Vol. 3021, pp. 175-189.
  9. Moon, B. R. (2008), Genetic Algorithm, Easy to Learn (In Korean), Hanbit Media, Seoul.
  10. Murata, T. and Ishibuchi, H. (1995), MOGA: Multi-objective genetic algorithms, Evolutionary Computation, 1995., IEEE International Conference on, 29 Nov-1 Dec, Perth, WA, Australia, Vol. 1, pp. 289.
  11. Murray, A. T., Kim, K., Davis, J. W., Machiraju, R., and Parent, R. (2007), Coverage optimization to support security monitoring. Computers, Environment and Urban Systems, Vol 31, No. 2, pp. 133-147. https://doi.org/10.1016/j.compenvurbsys.2006.06.002
  12. NGA (1995), Performance specification vector product interim terrain data, National Geographic Agency, Springfield, Virginia, http://earth-info.nga.mil/publications/specs/printed/89040a/89040A_VITD.pdf (last date accessed: 10 July 2016)
  13. Schaffer, J. D. (1985), Multiple objective optimization with vector evaluated genetic algoithms, Proceedings of the 1st international Conference on Genetic Algorithms, 1 Jul, Hillsdale, NJ, USA, pp. 93-100.
  14. Song, H. S., Park W. Y., Park, H. C., and Lee, Y. L. (2011), A comparison of LOS detection probability area for DEM and DSM, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 29, No. 2, pp. 165-173. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2011.29.2.165
  15. Yabuta, K. and Kitazawa, H. (2008), Optimum camera placement considering camera specification for security monitoring, 2008 IEEE International Symposium on Circuits and Systems, 18-21 May, Seattle, WA, USA, pp. 2114-2117.

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