Complex Motion Models for Simple Optical Flow Estimation

  • Claudia Nieuwenhuis
  • Daniel Kondermann
  • Christoph S. Garbe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)

Abstract

The selection of an optical flow method is mostly a choice from among accuracy, efficiency and ease of implementation. While variational approaches tend to be more accurate than local parametric methods, much algorithmic effort and expertise is often required to obtain comparable efficiency with the latter. Through the exploitation of natural motion statistics, the estimation of optical flow from local parametric models yields a good alternative. We show that learned, linear, parametric models capture specific higher order relations between neighboring flow vectors and, thus, allow for complex, spatio-temporal motion patterns despite a simple and efficient implementation. The method comes with an inherent confidence measure, and the motion models can easily be adapted to specific applications with typical motion patterns by choice of training data. The proposed approach can be understood as a generalization of the original structure tensor approach to the incorporation of arbitrary linear motion models. In this way accuracy, specificity, efficiency and ease of implementation can be achieved at the same time.

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References

  1. 1.
    Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision (DARPA). In: Proceedings of the DARPA Image Understanding Workshop, pp. 121–130 (1981)Google Scholar
  2. 2.
    Bigün, J., Granlund, G., Wiklund, J.: Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Journal of Pattern Analysis and Machine Intelligence 13(8), 775–790 (1991)CrossRefGoogle Scholar
  3. 3.
    Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)CrossRefGoogle Scholar
  4. 4.
    Baker, S., Roth, S., Scharstein, D., Black, M., Lewis, J., Szeliski, R.: A database and evaluation methodology for optical flow. In: Proceedings of ICCV, pp. 1–8 (2007)Google Scholar
  5. 5.
    Bruhn, A., Weickert, J., Feddern, C., Kohlberger, T., Schnörr, C.: Real-time optic flow computation with variational methods. IEEE Transactions in Image Processing 14(5), 608–615 (2005)CrossRefGoogle Scholar
  6. 6.
    Scharr, H.: Optimal filters for extended optical flow. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds.) IWCM 2004. LNCS, vol. 3417, Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Black, M., Jepson, A.: Estimating multiple independent motions in segmented images using parametric models with local deformations. In: IEEE Workshop on Motion of Non-Rigid and Articulated Objects (1994)Google Scholar
  9. 9.
    Ju, S.X., Black, M.J., Jepson, A.D.: Skin and bones: Multi-layer, locally affine, optical flow and regularization with transparency. In: CVPR (1996)Google Scholar
  10. 10.
    Black, M., Yacoob, Y.: Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion. In: Proceedings of the International Conference on Computer Vision, ICCV (1995)Google Scholar
  11. 11.
    Farnebäck, G.: Fast and accurate motion estimation using orientation tensors and parametric motion models. In: ICPR, vol. 1, pp. 135–139 (2000)Google Scholar
  12. 12.
    Nir, T., Bruckstein, A.M., Kimmel, R.: Over-parameterized variational optical flow. International Journal of Computer Vision 76(2), 205–216 (2006)CrossRefGoogle Scholar
  13. 13.
    Rudin, L., Osher, S.: Total variation based image restoration with free local constraints. In: ICIP, vol. 1, pp. 31–35 (1994)Google Scholar
  14. 14.
    Bredies, K., Kunish, K., Pock, T.: Total generalized variation, Techn. Rep. (2009)Google Scholar
  15. 15.
    Vlasenko, A., Schnörr, C.: Physically consistent and efficient variational denoising of image fluid flow estimates. IEEE Transact. Image Process. 19(3), 586–595 (2010)CrossRefGoogle Scholar
  16. 16.
    Haussecker, H., Fleet, D.: Computing optical flow with physical models of brightness variation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 23(6), 661–673 (2001)CrossRefGoogle Scholar
  17. 17.
    Gupta, S., Gupta, E.N., Prince, J.L.: Stochastic models for div-curl optical flow methods. IEEE Signal Processing Letters 3, 32–35 (1996)CrossRefGoogle Scholar
  18. 18.
    Cuzol, A., Hellier, P., Mémin, E.: A low dimensional fluid motion estimator. Int. J. Comp. Vision 75 (2007)Google Scholar
  19. 19.
    Roth, S., Black, M.: On the spatial statistics of optical flow. In: Proceedings of the International Conference on Computer Vision, vol. 1, pp. 42–49 (2005)Google Scholar
  20. 20.
    Black, M., Yacoob, Y., Jepson, A., Fleet, D.: Learning parameterized models of image motion. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, CVPR (1997)Google Scholar
  21. 21.
    Yacoob, Y., Davis, L.: Learned temporal models of image motion. In: Proceedings of the International Conference on Computer Vision (1998)Google Scholar
  22. 22.
    Nieuwenhuis, C., Kondermann, D., Jähne, B., Garbe, C.: An adaptive confidence measure for optical flows based on linear subspace projections. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 132–141. Springer, Heidelberg (2007)Google Scholar
  23. 23.
    Nieuwenhuis, C., Kondermann, D., Garbe, C.: Postprocessing of optical flows via surface measures and motion inpainting. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 355–364. Springer, Heidelberg (2008)Google Scholar
  24. 24.
    Suter, D.: Motion estimation and vector splines. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 939–942. IEEE, Los Alamitos (1994)CrossRefGoogle Scholar
  25. 25.
    McCane, B., Novins, K., Crannitch, D., Galvin, B.: On benchmarking optical flow. Computer Vision and Image Understanding 84(1), 126–143 (2001)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Claudia Nieuwenhuis
    • 1
  • Daniel Kondermann
    • 2
  • Christoph S. Garbe
    • 2
  1. 1.Technical University of MunichGermany
  2. 2.IWRUniversity of HeidelbergGermany

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