Advertisement

Dynamic Fire 3D Modeling Using a Real-Time Stereovision System

  • L. Rossi
  • M. Akhloufi
Conference paper

Abstract

This work presents a new framework for three-dimensional modeling of dynamic fires present in unstructured scenes. The proposed approach addresses the problem of segmenting fire regions using information from YUV and RGB color spaces. Clustering is also used to extract salient points from a pair of stereo images. These points are then used to reconstruct 3D positions in the scene. A matching strategy is proposed to deal with mismatches due to occlusions and missing data. The obtained data are fitted in a 3D ellipsoid in order to model the enclosing fire volume. This form is then used to compute dynamic fire characteristics like its position, dimension, orientation, heading direction, etc. These results are of great importance for fire behavior monitoring and prediction.

Keywords

Flame Front Stereo Image Stereo Vision Operational Scenario Fire Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgment

The present work was supported in part by the Corsican region under Grant ARPR 2007-2008.

References

  1. [1]
    C. Abecassis-Empis et al., “Characterisation of Dalmarnock fire Test One”, in Experimental Thermal and Fluid Science, 2008.Google Scholar
  2. [2]
    J. H. Balbi, J. L. Rossi, T. Marcelli, P. A. Santoni, “A 3D physical real-time model of surface fires across fuel beds”, in Combustion Science and Technology, vol. 179, 2007, pp. 2511-2537.CrossRefGoogle Scholar
  3. [3]
    J. Huseynov, S. Baliga, A. Widmer., Z. Boger, “2008 Special Issue: An adaptive method for industrial hydrocarbon flame detection" in Neural Networks 21(2-3): 398-405, 2008.CrossRefGoogle Scholar
  4. [4]
    C. M. Britton, B. L. Karr, F. A. Sneva, “Correlation of weather and fuel variables to mesquite damage by fire”, in Journ. of Range Manag., vol. 30, 1977, pp. 395-397CrossRefGoogle Scholar
  5. [5]
    H. B. Clements, “Measuring fire behavior with photography”, Photogram. Engineer. And Remote Sensing , vol. 49, n°10, 1983, pp. 1563-1575.Google Scholar
  6. [6]
    G. Lu, Y. Yan, Y Huang and A. Reed, “An Intelligent Monitoring and Control System of Combustion Flames”, Meas. Control , vol. 32, n°7, 1999, pp. 164–168.Google Scholar
  7. [7]
    D. X. Viegas, M.G.Cruz, L.M. Silva, A.J. Ollero, et al., “Forest Fire Research and Wildland Fire Safety, Proc. of IV Intern. Conf. on Forest Research and Wildland and Fire Safty Summit, 2002.Google Scholar
  8. [8]
    H. C. Bheemul, G. Lu and Y. Yan, “Digital Imaging Based Three-Dimensional Characterization of Flame Front Structures in a Turbulent Flame”, IEEE Trans. on Instr. And Measur., vol. 54, 2005, pp. 1073-1078.CrossRefGoogle Scholar
  9. [9]
    G. Lu, G. Gilabert and Y. Yan, “Three Dimensional Visualisation and Reconstruction of the Luminosity Distribution of a Flame using Digital Imaging Techniques”, Journ. of Physics: Conference Series, vol. 15, 2005, pp. 194-200CrossRefGoogle Scholar
  10. [10]
    S. W. Hasinoff and K.N. Kutulakos, “Photo-Consistent Reconstruction of Semitransparent Scenes by Density-Sheet Decomposition”, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 29, n°5, 2007, pp. 870-885.CrossRefGoogle Scholar
  11. [11]
    J. R. Martinez-de Dios, J.C . André, J.C.Gonçalves, B.Ch. Arrue, A. Ollero and D.X. Viegas, “Laboratory Fire Spread Analysis Using Visual and Infrared Cameras”, Inter. Journ.of Wildland Fire, vol. 15, 2006, pp.175-186.Google Scholar
  12. [12]
    E. Pastor, A. Àgueda, J. Andrade-Cetto, M. Muñoz, Y. Pérez, E. Planas, “Computing the rate of spread of linear flame fronts by thermal image processing”, Fire Safety Journal, vol. 41, n°8, 2006, pp. 569-579.CrossRefGoogle Scholar
  13. [13]
    L. Merino, F. Caballero and J.R. Martínez-de Dios, J. Ferruz, A. Ollero. “A cooperative perception system for multiple UAVs: Application to automatic detection of forest fire”, Journal of Field Robotics. vol. 23, n°3, John Wiley, 2006, pp. 165 - 184.CrossRefGoogle Scholar
  14. [14]
    J. R. Martinez-de Dios, B.C. Arrue, A. Ollero, L. Merino, F. Gomez-Rodriguez, “Computer vision techniques for forest fire perception, Image and vision computing, vol. 26, n°4, 2008, pp. 550-562.CrossRefGoogle Scholar
  15. [15]
    E. Trucco, A. Verri, "Introductory Techniques for 3-D Computer Vision", Prentice Hall, 1998.Google Scholar
  16. [16]
    O. Faugeras, "Three Dimensional Computer Vision", MIT Press, 1993.Google Scholar
  17. [17]
    R. Hartley, A. Zissermann, “Multiple view geometry in computer vision”, Cambridge university press, 2nd edition, 2003Google Scholar
  18. [18]
    A. Del Bue and L. Agapito. “Non-rigid Stereo Factorization”, International Journal of Computer Vision (IJCV), vol. 66, n°2, 2006, pp. 193-207.CrossRefGoogle Scholar
  19. [19]
    A. Del Bue, X. Llado and L. Agapito, “Non-rigid Face Modelling Using Shape Priors”, Analysis and Modelling of Faces and Gestures (AMFG 2005). Springer LNCS 3723.Google Scholar
  20. [20]
    A. Del Bue, L. Agapito, “Non-rigid 3D shape recovery using stereo factorization”. Asian Conference of Computer Vision (ACCV2004), 2004, vol. 1, pp. 25-30, 2004.Google Scholar
  21. [21]
    R. Hartley, A. Zissermann, “Multiple view geometry in computer vision”, Cambridge university press, 2nd edition, 2003Google Scholar
  22. [22]
    N Ayache, P.T. Sander, “Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception” MIT Press, 1991.Google Scholar
  23. [23]
    R. Hartley, “Stereo from uncalibrated cameras”, Proc. Of the Second Joint European – US Workshop on Applications of Invariance in Computer Vision, 1994, pp. 237-256.Google Scholar
  24. [24]
    Z. Zhang, “Determining the Epipolar Geometry and its Uncertainty: A Review”, International Journal of Computer Vision, vol. 27, n°2, 1998, pp. 161-198.CrossRefGoogle Scholar
  25. [25]
    Point Grey Research. http://www.ptgrey.com
  26. [26]
    S. Westland and A. Ripamonti, Computational Colour Science. John Wiley, 2004.Google Scholar
  27. [27]
    A. Tremeau, C. Fernandez-Maloigne, and P. Bonton, Image numérique couleur, Dunod, 2004.Google Scholar
  28. [28]
    M. J. Todd, and E. A. Yıldırım, “On Khachiyan's algorithm for the computation of minimum-volume enclosing ellipsoids”, Discrete Applied Mathematics, vol. 155, Issue 13, 2007, pp. 1731-1744.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • L. Rossi
    • 1
  • M. Akhloufi
    • 2
  1. 1.UMR CNRS SPE 6134University of CorsicaCorteFrance
  2. 2.CRVILévisCanada

Personalised recommendations