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On the Statistical Determination of Optimal Camera Configurations in Large Scale Surveillance Networks

  • Junbin Liu
  • Clinton Fookes
  • Tim Wark
  • Sridha Sridharan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

The selection of optimal camera configurations (camera locations, orientations etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we introduce a statistical formulation of the optimal selection of camera configurations as well as propose a Trans-Dimensional Simulated Annealing (TDSA) algorithm to effectively solve the problem. We compare our approach with a state-of-the-art method based on Binary Integer Programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than 2 alternative heuristics designed to deal with the scalability issue of BIP.

Keywords

Camera placement optimization resersible jump Markov chain Monte Carlo simulated annealing 

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References

  1. 1.
    Fookes, C., Denman, S., Lakemond, R., Ryan, D., Sridharan, S., Piccardi, M.: Semi-supervised intelligent surveillance system for secure environments. In: Proceedings of the IEEE Industrial Electronics Symposium, pp. 2815–2820 (2010)Google Scholar
  2. 2.
    Erdem, U.M., Sclaroff, S.: Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements. Computer Vision and Image Understanding 103(3), 156–169 (2006)CrossRefGoogle Scholar
  3. 3.
    Horster, E., Lienhart, R.: 5, Optimal Placement of Multiple Visual Sensors. In: Multi-Camera Networks: Concepts and Applications. Elsevier (2009)Google Scholar
  4. 4.
    Zhao, J., Cheung, S.C.S., Nguyen, T.: 6, Optimal Visual Sensor Network Configuration. In: Multi-Camera Networks: Concepts and Applications. Elsevier (2009)Google Scholar
  5. 5.
    Fleishman, S., Cohen-Or, D., Lischinski, D.: Automatic camera placement for image-based modeling. In: Proceedings of Seventh Pacific Conference on Computer Graphics and Applications, Fleishman 1999, pp. 12–20, 315 (1999)Google Scholar
  6. 6.
    Mordohai, P., Medioni, G.: Dense multiple view stereo with general camera placement using tensor voting. In: Proceedings of 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT 2004, pp. 725–732 (2004)Google Scholar
  7. 7.
    Olague, G., Mohr, R.: Optimal camera placement to obtain accurate 3d point positions. In: Proceedings of Fourteenth International Conference on Pattern Recognition, vol. 1, pp. 8–10 (1998)Google Scholar
  8. 8.
    Yao, Y., Chen, C.H., Abidi, B., Page, D., Koschan, A., Abidi, M.: Can you see me now? sensor positioning for automated and persistent surveillance. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40(1), 101–115 (2010)CrossRefGoogle Scholar
  9. 9.
    Bodor, R., Drenner, A., Schrater, P., Papanikolopoulos, N.: Optimal camera placement for automated surveillance tasks. Journal of Intelligent & Robotic Systems 50(3), 257–295 (2007)CrossRefGoogle Scholar
  10. 10.
    O’Rourke, J.: Art gallery theorems and algorithms. Oxford University Press, Inc. (1987)Google Scholar
  11. 11.
    Ram, S., Ramakrishnan, K.R., Atrey, P.K., Singh, V.K., Kankanhalli, M.S.: A design methodology for selection and placement of sensors in multimedia surveillance systems. In: Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks, 1178801, pp. 121–130. ACM (2006)Google Scholar
  12. 12.
    Mittal, A., Davis, L.S.: A general method for sensor planning in multi-sensor systems: Extension to random occlusion. Int. J. Comput. Vision 76(1), 31–52 (2008)CrossRefGoogle Scholar
  13. 13.
    Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6), 716–723 (1974)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Brooks, S.P., Friel, N., King, R.: Classical model selection via simulated annealing. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65(2), 503–520 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Andrieu, C., Freitas, N.d., Doucet, A.: Reversible jump mcmc simulated annealing for neural networks. In: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, 719898, pp. 11–18. Morgan Kaufmann Publishers Inc (2000)Google Scholar
  16. 16.
    Granville, V., Krivanek, M., Rasson, J.-P.: Simulated annealing: A proof of convergence. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6), 652–656 (1994)CrossRefGoogle Scholar
  17. 17.
    Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1985)CrossRefGoogle Scholar
  18. 18.
    Rissanen, J.: Stochastic complexity. Journal of the Royal Statistical Society. Series B (Methodological) 49(3), 223–239 (1987)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Kirkpatrick, S.: Optimization by simulated annealing: Quantitative studies. Journal of Statistical Physics 34(5), 975–986 (1984)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Green, P.J.: Reversible jump markov chain monte carlo computation and bayesian model determination. Biometrika 82(4), 711–732 (1995)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Junbin Liu
    • 1
  • Clinton Fookes
    • 1
  • Tim Wark
    • 2
  • Sridha Sridharan
    • 1
  1. 1.Image & Video Research LaboratoryQueensland University of TechnologyBrisbaneAustralia
  2. 2.CSIRO ICT CentrePullenvaleAustralia

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