Robust Optical Flow Estimation Using the Monocular Epipolar Geometry

  • Mahmoud A. MohamedEmail author
  • Bärbel Mertsching
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


The estimation of optical flow in cases of illumination change, sparsely-textured regions or fast moving objects is a challenging problem. In this paper, we analyze the use of a texture constancy constraint based on local descriptors (i.e., HOG) integrated with the monocular epipolar geometry to estimate robustly optical flow. The framework is implemented in differential data fidelities using a total variation model in a multi-resolution scheme. Besides, we propose an effective method to refine the fundamental matrix along with the estimation of the optical flow. Experimental results based on the challenging KITTI dataset show that the integration of texture constancy constraint with the monocular epipolar line constraint and the enhancement of the fundamental matrix significantly increases the accuracy of the estimated optical flow. Furthermore, a comparison with existing state-of-the-art approaches shows better performance for the proposed approach.


Optical flow Epipolar geometry HOG descriptor Fundamental matrix Texture constraint 


  1. 1.
    Molnár, J., Chetverikov, D., Fazekas, S.: Illumination-robust variational optical flow using cross-correlation. Comput. Vis. Image Underst. 114(10), 1104–1114 (2010)CrossRefGoogle Scholar
  2. 2.
    Werlberger, M., Pock, T., Bischof, H.: Motion estimation with non-local total variation regularization. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2464–2471 (2010)Google Scholar
  3. 3.
    Drulea, M., Nedevschi, S.: Motion estimation using the correlation transform. IEEE Trans. Image Process. 22(8), 3260–3270 (2013)CrossRefGoogle Scholar
  4. 4.
    Mueller, T., Rabe, C., Rannacher, J., Franke, U., Mester, R.: Illumination-robust dense optical flow using census signatures. In: Joint Pattern Recognition Symposium, pp. 236–245 (2011)Google Scholar
  5. 5.
    Rashwan, H.A., Mohamed, M.A., García, M.A., Mertsching, B., Puig, D.: Illumination robust optical flow model based on histogram of oriented gradients. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 354–363. Springer, Heidelberg (2013). Scholar
  6. 6.
    Mirabdollah, M.H., Mohamed, M.A., Mertsching, B.: Distributed averages of gradients (DAG): a fast alternative for histogram of oriented gradients. In: Behnke, S., Sheh, R., Sarıel, S., Lee, D.D. (eds.) RoboCup 2016. LNCS (LNAI), vol. 9776, pp. 97–108. Springer, Cham (2017). Scholar
  7. 7.
    Mohamed, M.A., Rashwan, H.A., Mertsching, B., García, M.A., Puig, D.: Illumination-robust optical flow using a local directional pattern. IEEE Trans. Circ. Syst. Video Technol. 24(9), 1499–1508 (2014)CrossRefGoogle Scholar
  8. 8.
    Mohamed, M.A., Rashwan, H.A., Mertsching, B., Garcia, M.A., Puig, D.: On improving the robustness of variational optical flow against illumination changes. In: Proceedings of the 4th ACM/IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream, pp. 1–8 (2013)Google Scholar
  9. 9.
    Senst, T., Geistert, J., Sikora, T.: Robust local optical flow: long-range motions and varying illuminations. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 4478–4482 (2016)Google Scholar
  10. 10.
    Kitt, B., Lategahn, H.: Trinocular optical flow estimation for intelligent vehicle applications. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 300–306 (2012)Google Scholar
  11. 11.
    Yamaguchi, K., McAllester, D., Urtasun, R.: Robust monocular epipolar flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1862–1869 (2013)Google Scholar
  12. 12.
    Valgaerts, L., Bruhn, A., Weickert, J.: A variational model for the joint recovery of the fundamental matrix and the optical flow. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 314–324. Springer, Heidelberg (2008). Scholar
  13. 13.
    Mohamed, M.A., Mirabdollah, M.H., Mertsching, B.: Monocular epipolar constraint for optical flow estimation. In: Liu, M., Chen, H., Vincze, M. (eds.) ICVS 2017. LNCS, vol. 10528, pp. 62–71. Springer, Cham (2017). Scholar
  14. 14.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  15. 15.
    Garrigues, M., Manzanera, A.: Fast semi dense epipolar flow estimation. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 427–435 (2017)Google Scholar
  16. 16.
    Mohamed, M.A., Mirabdollah, M.H., Mertsching, B.: Differential optical flow estimation under monocular epipolar line constraint. In: Nalpantidis, L., Krüger, V., Eklundh, J.-O., Gasteratos, A. (eds.) ICVS 2015. LNCS, vol. 9163, pp. 354–363. Springer, Cham (2015). Scholar
  17. 17.
    Ranftl, R., Vineet, V., Chen, Q., Koltun, V.: Dense monocular depth estimation in complex dynamic scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4058–4066 (2016)Google Scholar
  18. 18.
    Kennedy, R., Taylor, C.J.: Optical flow with geometric occlusion estimation and fusion of multiple frames. In: Tai, X.-C., Bae, E., Chan, T.F., Lysaker, M. (eds.) EMMCVPR 2015. LNCS, vol. 8932, pp. 364–377. Springer, Cham (2015). Scholar
  19. 19.
    Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.GET LabUniversity of PaderbornPaderbornGermany

Personalised recommendations