A Grey Wolf Optimization Based Algorithm for Optimum Camera Placement

  • Ajay KaushikEmail author
  • S. Indu
  • Daya Gupta


Camera placement is very important for surveillance applications. Proposed work presents a new method of optimum placement of visual sensors for maximum coverage of the predefined surveillance space. The surveillance space is modeled as priority areas (PAs), obstacles and feasible locations for placement of cameras. We are using PTZ (pan, tilt, zoom) cameras, which not only reduces occlusion due to randomly moving objects in the PA but also increases the covered area compared to pin hole cameras. The proposed approach will be useful for crowd monitoring in a big surveillance space holding multiple events and having multiple entrances. The problem of optimum camera placement for maximum coverage considering both static and randomly moving obstacles is mapped as a Grey Wolf Optimization (GWO) problem. The proposed algorithm is computationally lighter and converges faster as compared to Genetic Algorithm (GA) based camera placement and Particle Swarm Optimization (PSO) based camera placement algorithm. The concept is validated using simulation as well as the experimental results.


Camera placement GWO Obstacles Pan Tilt Zoom Priority area Visual sensors FoV Surveillance 



We acknowledge the suggestions and help of Prof. Santanu Chaudhury (IIT Delhi) to this work.


  1. 1.
    Astaras, S., et al. (2017). Visual detection of events of interest from urban activity. Wireless Personal Communications, 97(2), 1877–1888.Google Scholar
  2. 2.
    Lubobya, S. C., et al. (2018). Mesh IP video surveillance systems model design and performance evaluation. Wireless Personal Communications, 100(2), 227–240.Google Scholar
  3. 3.
    Yabuta, K., & Kitazawa, H. (2008). Optimum camera placement considering camera specification for security monitoring, In International symposium on circuits and systems, IEEE, (pp. 2114–2117).Google Scholar
  4. 4.
    Indu, S. et al. (2009). Optimal sensor placement for surveillance of large spaces. In 3rd ACM/IEEE international conference on distributed smart cameras, (pp. 1–8).Google Scholar
  5. 5.
    Rourke, J. O. (1987). Art gallery theorems and algorithms. Oxford: Oxford University Press.Google Scholar
  6. 6.
    Indu, S., Garg, R., & Chudhury, S. (2011). Camera and light source placement, a multi objective approach. In 3rd national conference on computer vision pattern recognition image processing and graphics. IEEE, (pp. 187–191).Google Scholar
  7. 7.
    Xu, Y. C. L., Lei, L., & Hendricks, E. A. (2011). Camera network coverage Improving by PSO (p. 458283). EURASIP Journal on Image and Video Processing: Springer.Google Scholar
  8. 8.
    Mirjalili, S., et al. (2014). A Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.Google Scholar
  9. 9.
    Kumar, G., & Ranga, V. (2017). Healing Partitioned WSNs, Ubiquitous Computing and Ambient Intelligence. Lecture Notes in Computer Science, 10586, 545–557.Google Scholar
  10. 10.
    Kumar, G., & Ranga, V. (2017). Meta-heuristic solution for relay nodes placement in constrained environment. In 10th International Conference on Contemporary Computing, IEEE.Google Scholar
  11. 11.
    Saunhita, S., et al. (2018). Optimized relay nodes positioning to achieve full connectivity in WSNs. Wireless Personal Communications, 99(4), 1521–1540.Google Scholar
  12. 12.
    Zhao, C., et al. (2007). PSO for optimal deployment of relay nodes in hybrid sensor networks. In IEEE congress on evolutionary computation, (pp. 3316–3320).Google Scholar
  13. 13.
    Hashim, H. A., et al. (2016). Optimal placement of relay nodes in WSN using ABC algorithm. Journal of Network and Computer Applications, 64, 239–248.Google Scholar
  14. 14.
    Sujitha, J., & Baskaran, K. (2018). Genetic GWO based channel estimation in wireless communication system. Wireless Personal Communications, 99(2), 965–984.Google Scholar
  15. 15.
    Mohanty, S., et al. (2016). A new MPPT design using GWO technique for photovoltaic system under partial shading conditions. IEEE Transactions on Sustainable Energy, 7(1), 181–188.Google Scholar
  16. 16.
    Banu, S. S., & Baskaran, K. (2018). Hybrid FGWO based FLCs modeling for performance enhancement in wireless body area networks. Wireless Personal Communications, 100(3), 1163–1199.Google Scholar
  17. 17.
    Li, L., et al. (2017). Modified discrete grey wolf optimizer algorithm for multilevel image thresholding. Computational Intelligence and Neuroscience. Scholar
  18. 18.
    Suzuki, I., et al. (2001). Searching a polygonal region from the boundary. International Journal of Computation Geometry and Applications, 11(5), 529–553.MathSciNetzbMATHGoogle Scholar
  19. 19.
    Bose, P., et al. (1997). The flood light problem. International Journal of Computation Geometry and Applications, 7(1), 153–163.Google Scholar
  20. 20.
    Castro, V. E., & Rourke, J. O. (1995). Illumination of polygons with vertex lights. Information Process Letter, 56(1), 9–13.MathSciNetzbMATHGoogle Scholar
  21. 21.
    Chen, X., & Davis, J. (1999). Camera placement considering occlusion for robust motion capture, Technical Report CS-TR-2000-07.Google Scholar
  22. 22.
    Mittal, A., & Davis, L, (2004). Visibility analysis and sensor planning in dynamic environment. In European conference on computer vision. Springer, 175–189.Google Scholar
  23. 23.
    Horster, E., & Lienhart, R. (2006). Calibrating and optimizing pose of visual sensors in distributed platforms. ACM Multimedia Systems Journal, 12(3), 195–210.Google Scholar
  24. 24.
    Kankanhalli, M. et al. (2006). A design methodology for selection and placement of sensors in multimedia systems. In Proceedings of VSSN-06, (pp. 121–130).Google Scholar
  25. 25.
    Astaras, S., et al. (2017). Visual detection of events of interest from urban activity. Wireless Personal Commununicaions, 97(3), 1877–1888. Scholar
  26. 26.
    Olague, G., & Dunn, E. (2007). Development of a practical photogrammatic network design using evolutionary computing. Photogrammetric Record, 22(117), 22–38.Google Scholar
  27. 27.
    Nam, Y., Hong, S. (2012). Optimal placement of multiple visual sensors using simulation of pedestrian movement. In International Conference on Computing, Networking And Communications, (pp. 67–71).Google Scholar
  28. 28.
    Topcuoglu, H., et al. (2011). Positioning and utilizing sensors on a 3d terrain part i-theory and modeling. IEEE Transactions on Systems Man Cybernetics Part C, 41(3), 376–382.Google Scholar
  29. 29.
    Mishra, R. et al. (2013). Monitoring a large surveillance space through distributed face matching. In 4th National Conference on Comp. Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), (pp. 1–5).Google Scholar
  30. 30.
    Liu, Junbin, et al. (2014). Optimal camera planning under versatile user constraints in multi-camera image processing systems. IEEE Transactions on Image Processing, 23(1), 177–184.MathSciNetzbMATHGoogle Scholar
  31. 31.
    Jun-Woo, A., et al. (2016). Two-phase algorithm for optimal camera placement. Scientific Programming. Scholar
  32. 32.
    Sungbum, J. (2018). Placing visual sensors using heuristic algorithms for bridge surveillance. Applied Sciences, 8(1), 70. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and EngineeringDelhi Technological UniversityDelhiIndia
  2. 2.Electronics and Communication EngineeringDelhi Technological UniversityDelhiIndia

Personalised recommendations