Skip to main content
SpringerLink
Log in

Dense Motion Estimation for Smoke

  • Conference paper
  • First Online:
Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10114))

Included in the following conference series:

Abstract

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.

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

Access this chapter

Log in via an institution
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

Institutional subscriptions

References

  1. Gregson, J., Ihrke, I., Thuerey, N., Heidrich, W., et al.: From capture to simulation-connecting forward and inverse problems in fluids. ACM Trans. Graph. 33, 1 (2014)

    Article  Google Scholar 

  2. Okabe, M., Anjyo, K., Onai, R.: Extracting fluid from a video for efficient post-production. In: Proceedings of the Digital Production Symposium, pp. 53–58. ACM (2012)

    Google Scholar 

  3. Ghanem, B., Ahuja, N.: Extracting a fluid dynamic texture and the background from video. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)

    Google Scholar 

  4. Li, C., Pickup, D., Saunders, T., Cosker, D., Marshall, D., Hall, P., Willis, P.: Water surface modeling from a single viewpoint video. IEEE Trans. Vis. Comput. Graph. 19, 1242–1251 (2013)

    Article  Google Scholar 

  5. Lakshmanan, V., Rabin, R., DeBrunner, V.: Multiscale storm identification and forecast. Atmos. Res. 67, 367–380 (2003)

    Article  Google Scholar 

  6. Vila, D.A., Machado, L.A.T., Laurent, H., Velasco, I.: Forecast and tracking the evolution of cloud clusters (ForTraCC) using satellite infrared imagery: methodology and validation. Weather Forecast. 23, 233–245 (2008)

    Article  Google Scholar 

  7. Barbaresco, F., Monnier, B.: Rain clouds tracking with radar image processing based on morphological skeleton matching. In: 2001 International Conference on Image Processing, vol. 1, pp. 830–833, Proceedings. IEEE (2001)

    Google Scholar 

  8. Garcia, D.: Robust smoothing of gridded data in one and higher dimensions with missing values. Comput. Stat. Data Anal. 54, 1167–1178 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  9. Wang, G., Garcia, D., Liu, Y., De Jeu, R., Dolman, A.J.: A three-dimensional gap filling method for large geophysical datasets: application to global satellite soil moisture observations. Environ. Model. Softw. 30, 139–142 (2012)

    Article  Google Scholar 

  10. Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: EpicFlow: edge-preserving interpolation of correspondences for optical flow. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1164–1172 (2015)

    Google Scholar 

  11. Horn, B.K., Schunck, B.G.: Determining optical flow. In: 1981 Technical Symposium East, International Society for Optics and Photonics, pp. 319–331 (1981)

    Google Scholar 

  12. Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 611–625. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33783-3_44

    Chapter  Google Scholar 

  13. Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 500–513 (2011)

    Article  Google Scholar 

  14. Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput. Vis. Image Underst. 63, 75–104 (1996)

    Article  Google Scholar 

  15. Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2432–2439. IEEE (2010)

    Google Scholar 

  16. Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: FlowNet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758–2766 (2015)

    Google Scholar 

  17. Mémin, E., Pérez, P.: Fluid motion recovery by coupling dense and parametric vector fields. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 620–625. IEEE (1999)

    Google Scholar 

  18. Auroux, D., Fehrenbach, J.: Identification of velocity fields for geophysical fluids from a sequence of images. Exp. Fluids 50, 313–328 (2011)

    Article  Google Scholar 

  19. Corpetti, T., Mémin, E., Pérez, P.: Estimating fluid optical flow. In: 15th International Conference on Pattern Recognition, vol. 3, pp. 1033–1036, Proceedings. IEEE (2000)

    Google Scholar 

  20. Corpetti, T., Mémin, É., Pérez, P.: Dense estimation of fluid flows. IEEE Trans. Pattern Anal. Mach. Intell. 24, 365–380 (2002)

    Article  MATH  Google Scholar 

  21. Corpetti, T., Heitz, D., Arroyo, G., Memin, E., Santa-Cruz, A.: Fluid experimental flow estimation based on an optical-flow scheme. Exp. Fluids 40, 80–97 (2006)

    Article  Google Scholar 

  22. Anumolu, L., Ahmed, I.: Simulation of smoke in OpenFOAM framework (2012)

    Google Scholar 

  23. Li, F., Xu, L., Guyenne, P., Yu, J.: Recovering fluid-type motions using navier-stokes potential flow. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, IEEE 2448–2455(2010)

    Google Scholar 

  24. Doshi, A., Bors, A.G.: Robust processing of optical flow of fluids. IEEE Trans. Image Process. 19, 2332–2344 (2010)

    Article  MathSciNet  Google Scholar 

  25. Steinhoff, J., Underhill, D.: Modification of the Euler equations for vorticity confinement: application to the computation of interacting vortex rings. Phys. Fluids 6, 2738–2744 (1994)

    Article  MATH  Google Scholar 

  26. Sakaino, H.: Fluid motion estimation method based on physical properties of waves. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)

    Google Scholar 

  27. Ji, Y., Ye, J., Yu, J.: Reconstructing gas flows using light-path approximation. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2507–2514. IEEE (2013)

    Google Scholar 

  28. Xue, T., Rubinstein, M., Wadhwa, N., Levin, A., Durand, F., Freeman, W.T.: Refraction wiggles for measuring fluid depth and velocity from video. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 767–782. Springer, Cham (2014). doi:10.1007/978-3-319-10578-9_50

    Google Scholar 

  29. Ding, Y., Li, F., Ji, Y., Yu, J.: Dynamic fluid surface acquisition using a camera array. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2478–2485. IEEE (2011)

    Google Scholar 

  30. Teney, D., Brown, M., Kit, D., Hall, P.: Learning similarity metrics for dynamic scene segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2084–2093 (2015)

    Google Scholar 

  31. Chen, J., Zhao, G., Salo, M., Rahtu, E., Pietikainen, M.: Automatic dynamic texture segmentation using local descriptors and optical flow. IEEE Trans. Image Process. 22, 326–339 (2013)

    Article  MathSciNet  Google Scholar 

  32. Haindl, M., Mikeš, S.: Unsupervised dynamic textures segmentation. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013. LNCS, vol. 8047, pp. 433–440. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40261-6_52

    Chapter  Google Scholar 

  33. Chan, A.B., Vasconcelos, N.: Variational layered dynamic textures. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1062–1069, CVPR 2009. IEEE (2009)

    Google Scholar 

  34. Strahler, A.N.: Quantitative analysis of watershed geomorphology. EOS Trans. Am. Geophys. Union 38, 913–920 (1957)

    Article  Google Scholar 

  35. Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vis. 13, 119–152 (1994)

    Article  Google Scholar 

  36. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846. IEEE (1998)

    Google Scholar 

  37. Strang, G.: The discrete cosine transform. SIAM Rev. 41, 135–147 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  38. Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24673-2_3

    Chapter  Google Scholar 

  39. Xu, L., Jia, J., Matsushita, Y.: Motion detail preserving optical flow estimation. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1744–1757 (2012)

    Article  Google Scholar 

  40. Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. 92, 1–31 (2011)

    Article  Google Scholar 

Download references

Acknowledgement

The authors are supported by the EPSRC project OAK EP/K02339X/1. We thank the reviewers for constructive comments, and also thank H. Gong for his helpful suggestion and comments.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bath, Bath, UK

    Da Chen & Peter Hall

  2. Department of Computer Science, University College London, London, UK

    Wenbin Li

Authors
  1. Da Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenbin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Hall
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Da Chen .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Chen, D., Li, W., Hall, P. (2017). Dense Motion Estimation for Smoke. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10114. Springer, Cham. https://doi.org/10.1007/978-3-319-54190-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54190-7_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54189-1

  • Online ISBN: 978-3-319-54190-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation