Wood Detection and Tracking in Videos of Rivers

  • Imtiaz Ali
  • Julien Mille
  • Laure Tougne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


Rivers during floods bring a lot of fallen trees and debris. Video surveillance systems are installed on strategically important places on the rivers. To protect these places from destructions due to accumulation of wood, such systems must be able to automatically detect wood. Image segmentation is performed to separate wood and other moving elements from the rest of the water. Moving objects are detected with respect to brightness and temporal variation features. The floating wood is then tracked in the sequence of frames by temporal linking of the segments generated in the detection step. Our algorithm is tested on multiple videos of floods and the results are evaluated both qualitatively and quantitatively.


Image Segmentation Gaussian Mixture Model Water Wave Consecutive Frame Representative Point 
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.


  1. 1.
    Shah, M.: Motion-based recognition: A survey. Image and Vision Computing 13, 129–155 (1995)CrossRefGoogle Scholar
  2. 2.
    Li, L., Huang, W.M., Gu, I.Y.H., Tian, Q.: Statistical modeling of complex background for foreground object detection. IEEE Trans. on Image Processing 13(11), 1459–1472 (2004)CrossRefGoogle Scholar
  3. 3.
    Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal Machine Intell. 22(8), 747–757 (2000)CrossRefGoogle Scholar
  4. 4.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: realtime tracking of the human body. IEEE Trans. on Pattern Anal. Machine Intell. 19(7), 780–785 (1997)CrossRefGoogle Scholar
  5. 5.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practices of background maintenance. In: IEEE Int. Conf. Computer Vision (ICCV), pp. 255–261 (1999)Google Scholar
  6. 6.
    Eng, H.L., Wang, J., Wah, A.H.K.S., Yau, W.Y.: Robust human detection within a highly dynamic aquatic environment in real time. IEEE Trans. on Image Processing 15(6), 1583–1600 (2006)CrossRefGoogle Scholar
  7. 7.
    Mittal, A., Paragios, N.: Motion-based background subtraction using adaptive kernel density estimation. In: IEEE Conf. Comp. Vision and Pattern Recog. (CVPR), pp. 302–309 (2004)Google Scholar
  8. 8.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  9. 9.
    Wixson, L.: Detecting salient motion by accumulating directionally-consistent flow. IEEE Trans. Pattern Anal Machine Intell. 22(8), 774–780 (2000)CrossRefGoogle Scholar
  10. 10.
    Gao, X., Boult, T., Coetzee, F., Ramesh, V.: Error analysis of background adoption. In: IEEE Conf. Comp. Vision and Pattern Recog. (CVPR), pp. 503–510 (June 2000)Google Scholar
  11. 11.
    Ali, I., Tougne, L.: Unsupervised video analysis for counting of wood in river during floods. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5876, pp. 578–587. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Cárdenes, R., Bach, M., Chi, Y., Marras, I., de Luis, R., Anderson, M., Cashman, P., Bultelle, M.: Multimodal evaluation for medical image segmentation. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 229–236. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Imtiaz Ali
    • 1
    • 2
  • Julien Mille
    • 1
    • 3
  • Laure Tougne
    • 1
    • 2
  1. 1.Université de Lyon, CNRSFrance
  2. 2.LIRIS, UMR5205Université Lyon 2France
  3. 3.LIRIS, UMR5205Université Lyon 1France

Personalised recommendations