Optical Flow Estimation on Omnidirectional Images: An Adapted Phase Based Method

  • Brahim Alibouch
  • Amina Radgui
  • Mohammed Rziza
  • Driss Aboutajdine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)


Omnidirectional vision is one of emerging areas of research. Omnidirectional images offer a large field of view compared to conventional perspectives images. However, these images contain important distortions, and classical optical flow estimation are thus not appropriate. In this paper, we propose to estimate optical flow on omnidirectional images using a phase based method which proved its robustness and its accuracy on the perspective images. We will adapt different treatments that this method involve in order to take into account the nature of omnidirectional images.


optical flow omnidirectional vision phase based methods component velocity Gabor filters 


  1. 1.
    Beauchemin, S.S., Barron, J.L.: The Computation of Optical Flow. ACM Comput. Surv. 27, 433–467 (2003)CrossRefGoogle Scholar
  2. 2.
    Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  3. 3.
    Radgui, A., Demonceaux, C., Mouaddib, E., Rziza, M., Aboutajdine, D.: Optical flow estimation from multichannel spherical image decomposition. Computer Vision and Image Understanding 115, 1263–1272 (2011)CrossRefGoogle Scholar
  4. 4.
    Kim, J., Suga, Y.: An omnidirectional vision-based moving obstacle detection in mobile robot. International Journal of Control Automation and Systems 5, 663–673 (2007)Google Scholar
  5. 5.
    Yoshizaki, W., Mochizuki, Y., Ohnishi, N., Imiya, A.: Catadioptric omnidirectional images for visual navigation using optical flow. In: OMNIVIS 2008 (2008)Google Scholar
  6. 6.
    Winters, N., Gaspar, J., Lacey, G., Santos-Victor, J.: Omni-directional vision for robot navigation. In: IEEE Workshop on Omnidirectional Vision, pp. 21–28 (2000)Google Scholar
  7. 7.
    Wang, M.L., Huang, C.C., Lin, H.Y.: An intelligent surveillance system based on an omnidirectional vision sensor. In: IEEE Conference on Cybernetics and Intelligent Systems, pp. 1–6 (2006)Google Scholar
  8. 8.
    Gluckman, J., Nayar, S.: Ego-motion and omnidirectional cameras. In: IEEE International Conference on Computer Vision (ICCV), pp. 999–1005 (1998)Google Scholar
  9. 9.
    Bunschoten, R., Krose, B.: Visual odometry from an omnidirectional vision system. In: IEEE International Conference on Robotics and Automation (ICRA 2003), vol. 1, pp. 577–583 (2003)Google Scholar
  10. 10.
    Barron, J.L., Fleet, D.J., Beauchemin, S.: Performance of optical flow techniques. Int. J. Comput. Vis. 12, 43–77 (1994)CrossRefGoogle Scholar
  11. 11.
    Kanade, T., Lucas, B.: An iterative image registration technique with an application to stereo vision. In: IJCAI 1981, pp. 674–679 (1981)Google Scholar
  12. 12.
    Nagel, H.H.: On a constraint equation for the estimation of displacement rates in image sequences. IEEE Transaction on Pattern Analysis and Machine Intelligence 11, 13–30 (1989)zbMATHCrossRefGoogle Scholar
  13. 13.
    Fleet, D.J., Jepson, A.D.: Computation of component image velocity from local phase information. Int. J. Comput. Vis. 5, 77–104 (1990)CrossRefGoogle Scholar
  14. 14.
    Tsao, T., Chen, V.: A neural scheme for optical flow computation based on Gabor filters and generalized gradient method. Neurocomputing 6, 305–325 (1994)zbMATHCrossRefGoogle Scholar
  15. 15.
    Anandan, P.: A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision 2, 283–310 (1989)CrossRefGoogle Scholar
  16. 16.
    Adelson, E., Bergen, J.: Spatiotemporal energy models for the perception of motion. Journal of Optical Society of America 2, 284–299 (1985)CrossRefGoogle Scholar
  17. 17.
    Heeger, D.: Optical flow using spatiotemporal filters. International Journal of Computer Vision 1, 279–302 (1988)CrossRefGoogle Scholar
  18. 18.
    Gautama, T., Van Hulle, M.M.: A phase-based approach to the estimation of the optical flow field using spatial filtering. IEEE Trans. Neural Networks 13, 1127–1136 (2002)CrossRefGoogle Scholar
  19. 19.
    Pauwels, K., Van Hulle, M.M.: Optic Flow from Unstable Sequences containing Unconstrained Scenes through Local Velocity Constancy Maximization. In: BMVC, pp. 397–406 (2006)Google Scholar
  20. 20.
    Pauwels, K., Van Hulle, M.M.: Realtime phase-based optical flow on the GPU. In: Computer Vision and Pattern Recognition Workshops, pp. 1–8 (2008)Google Scholar
  21. 21.
    Geyer, C., Daniilidis, K.: Catadioptric projective geometry. Int. J. Comput. Vis. 43, 223–243 (2001)CrossRefGoogle Scholar
  22. 22.
    Demanet, L., Vandergheynst, P.: Gabor wavelets on the sphere. In: SPIE Conference on Wavelets: Applications in Signal and Image Processing (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Brahim Alibouch
    • 1
  • Amina Radgui
    • 1
    • 2
  • Mohammed Rziza
    • 1
  • Driss Aboutajdine
    • 1
  1. 1.LRIT associated unit with CNRST (URAC29)Mohammed V-Agdal UniversityRabatMorocco
  2. 2.INPTRabatMorocco

Personalised recommendations