Fast Dynamic Texture Detection

  • V. Javier Traver
  • Majid Mirmehdi
  • Xianghua Xie
  • Raúl Montoliu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

Dynamic textures can be considered to be spatio-temporally varying visual patterns in image sequences with certain temporal regularity. We propose a novel and efficient approach to explore the violation of the brightness constancy assumption, as an indication of presence of dynamic texture, using simple optical flow techniques. We assume that dynamic texture regions are those that have poor spatio-temporal optical flow coherence. Further, we propose a second approach that uses robust global parametric motion estimators that effectively and efficiently detect motion outliers, and which we exploit as powerful cues to localize dynamic textures. Experimental and comparative studies on a range of synthetic and real-world dynamic texture sequences show the feasibility of the proposed approaches, with results which are competitive to or better than recent state-of-art approaches and significantly faster.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

978-3-642-15561-1_49_MOESM1_ESM.avi (3.5 mb)
Electronic Supplementary Material (3,610 KB)

References

  1. 1.
    Chetverikov, D., Péteri, R.: A brief survey of dynamic texture description and recognition. In: Proc. Intl. Conf. Computer Recognition Systems, pp. 17–26 (2005)Google Scholar
  2. 2.
    Péteri, R., Chetverikov, D.: Dynamic texture recognition using normal flow and texture regularity. In: IbPRIA, pp. 223–230 (2005)Google Scholar
  3. 3.
    Doretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. IJCV 51, 91–109 (2003)MATHCrossRefGoogle Scholar
  4. 4.
    Atcheson, B., Heidrich, W., Ihrke, I.: An evaluation of optical flow algorithms for background oriented schlieren imaging. Experiments in Fluids 46, 467–476 (2009)CrossRefGoogle Scholar
  5. 5.
    Vezzani, R., Calderara, S., Piccinini, P., Cucchiara, R.: Smoke detection in video surveillance: The use of ViSOR. In: ACM IVR, pp. 289–297 (2008)Google Scholar
  6. 6.
    Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE-PAMI 29, 915–928 (2007)Google Scholar
  7. 7.
    Saisan, P., Doretto, G., Wu, Y.N., Soatto, S.: Dynamic texture recognition. In: CVPR, vol. 2, pp. 58–63 (2001)Google Scholar
  8. 8.
    Hyndman, M., Jepson, A., Fleet, D.: Higher-order autoregressive models for dynamic textures. In: BMVC (2007)Google Scholar
  9. 9.
    Chan, A.B., Vasconcelos, N.: Variational layered dynamic textures. In: CVPR (2009)Google Scholar
  10. 10.
    Vidal, R., Ravichandran, A.: Optical flow estimation and segmentation of multiple moving dynamic textures. In: CVPR, pp. 516–521 (2005)Google Scholar
  11. 11.
    Chan, A., Vasconcelos, N.: Layered dynamic textures. IEEE-PAMI 31, 1862–1879 (2009)Google Scholar
  12. 12.
    Doretto, G., Cremers, D., Favaro, P., Soatto, S.: Dynamic texture segmentation. In: ICCV, vol. 2, pp. 1236–1242 (2003)Google Scholar
  13. 13.
    Campisi, P., Maiorana, E., Neri, A., Scarano, G.: Video texture modelling and synthesis using fractal processes. IET Image Processing 2, 1–17 (2008)CrossRefGoogle Scholar
  14. 14.
    Lu, Z., Xie, W., Pei, J., Huang, J.: Dynamic texture recognition by spatiotemporal multiresolution histograms. In: IEEE Workshop. on Motion & Video Computing, vol. 2, pp. 241–246 (2005)Google Scholar
  15. 15.
    Ghanem, B., Ahuja, N.: Extracting a fluid dynamic texture and the background from video. In: CVPR (2008)Google Scholar
  16. 16.
    Toreyin, B., Cetin, A.: HMM based method for dynamic texture detection. In: IEEE 15th. Signal Processing and Communications Applications (2007)Google Scholar
  17. 17.
    Ferrari, R.J., Zhang, H., Kube, C.R.: Real-time detection of steam in video images. PR 40, 1148–1159 (2007)MATHGoogle Scholar
  18. 18.
    Xiong, X., Caballero, R., Wang, H., Finn, A.M., Lelic, M.A., Peng, P.Y.: Video-based smoke detection: Possibilities, techniques, and challenges. In: IFPA (2007)Google Scholar
  19. 19.
    Toreyin, B., Cetin, A.: On-line detection of fire in video. In: CVPR (2007)Google Scholar
  20. 20.
    Corpetti, T., Memin, E., Pérez, P.: Dense estimation of fluid flows. IEEE-PAMI 24, 365–380 (2002)Google Scholar
  21. 21.
    Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Wavelet based real-time smoke detection in video. In: EUSIPCO (2005)Google Scholar
  22. 22.
    Rahman, A., Murshed, M.: Real-time temporal texture characterisation using block based motion co-occurrence statistics. In: ICIP, pp. III: 1593–1596 (2004)Google Scholar
  23. 23.
    Bouthemy, P., Hardouin, C., Piriou, G., Yao, J.: Mixed-state auto-models and motion texture modeling. JMIV 25, 387–402 (2006)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Fazekas, S., Chetverikov, D.: Analysis and performance evaluation of optical flow features for dynamic texture recognition. Signal Processing: Image Comm. 22, 680–691 (2007)CrossRefGoogle Scholar
  25. 25.
    Fazekas, S., Amiaz, T., Chetverikov, D., Kiryati, N.: Dynamic texture detection based on motion analysis. IJCV 82, 48–63 (2009)CrossRefGoogle Scholar
  26. 26.
    Viola, P.A., Jones, M.J.: Robust real-time face detection. IJCV 57, 137–154 (2004)CrossRefGoogle Scholar
  27. 27.
    Odobez, J., Bouthemy, P.: Robust multiresolution estimation of parametric motion models. Int. J. Visual Communication and Image Representation 6, 348–365 (1995)CrossRefGoogle Scholar
  28. 28.
    Chetverikov, D., Fazekas, S., Haindl, M.: Dynamic texture as foreground and background. In: MVA (2010), doi:10.1007/s00138-010-0251-6 (Published online: February 21, 2010)Google Scholar
  29. 29.
    Fazekas, S., Amiaz, T., Chetverikov, D., Kiryati, N.: (Dynamic texture detection and segmenation), http://vision.sztaki.hu/~fazekas/dtsegm
  30. 30.
    Péteri, R., Huskies, M., Fazekas, S. (DynTex: a comprehensive database of dynamic textures), http://www.cwi.nl/projects/dyntex

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • V. Javier Traver
    • 1
    • 2
  • Majid Mirmehdi
    • 4
  • Xianghua Xie
    • 5
  • Raúl Montoliu
    • 2
    • 3
  1. 1.DLSI 
  2. 2.iNIT 
  3. 3.DICC, Univ. Jaume I, CastellónSpain
  4. 4.Dept. Comp. ScienceUniv. of BristolBristolUK
  5. 5.Dept. Comp. ScienceUniv. of Wales SwanseaSwanseaUK

Personalised recommendations