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Improving Motion Estimation Using Image-Driven Functions and Hybrid Scheme

  • Duc Dung Nguyen
  • Jae Wook Jeon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)

Abstract

We introduce an alternative method to improve optical flow estimation using image data for control functions. Base on the nature of object motion, we tune the energy minimization process with an image-adaptive scheme embedded inside the energy function. We propose a hybrid scheme to improve the quality of the flow field and we use it along with the multiscale approach to deal with large motion in the sequence. The proposed hybrid scheme take advantages from multigrid solver and the pyramid model. Our proposed method yields good estimation results and it shows the potential to improve the performance of a given model. It can be applied to other advanced models. By improving quality of motion estimation, various applications in intelligent systems are available such as gesture recognition, video analysis, motion segmentation, etc.

Keywords

Optical Flow Motion Estimation Gesture Recognition Hybrid Scheme Angular Error 
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.

References

  1. 1.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17(1-3), 185–203 (1981)CrossRefGoogle Scholar
  2. 2.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI, pp. 674–679 (April 1981)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Xu, L., Chen, J., Jia, J.: A Segmentation Based Variational Model for Accurate Optical Flow Estimation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 671–684. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Zimmer, H., Bruhn, A., Weickert, J., Valgaerts, L., Salgado, A., Rosenhahn, B., Seidel, H.-P.: Complementary Optic Flow. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds.) EMMCVPR 2009. LNCS, vol. 5681, pp. 207–220. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Wedel, A., Pock, T., Braun, J., Franke, U., Cremers, D.: Duality tv-l1 flow with fundamental matrix prior. In: Image and Vision Computing New Zealand (2008)Google Scholar
  7. 7.
    Werlberger, M., Pock, T., Bischof, H.: Motion estimation with non-local total variation regularization. In: CVPR, pp. 2464–2471 (2010)Google Scholar
  8. 8.
    Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An Improved Algorithm for TV-L1 Optical Flow. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 23–45. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic huber-l1 optical flow. In: BMVC, British Machine Vision Association (2009)Google Scholar
  10. 10.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. In: ICCV, pp. 1–8 (2007)Google Scholar
  11. 11.
    Bruhn, A., Weickert, J.: Towards ultimate motion estimation: combining highest accuracy with real-time performance. In: ICCV, vol. 1, pp. 749–755 (October 2005)Google Scholar
  12. 12.
    Papenberg, N., Bruhn, A., Brox, T., Didas, S., Weickert, J.: Highly accurate optic flow computation with theoretically justified warping. Int. J. Comput. Vision 67(2), 141–158 (2006)CrossRefGoogle Scholar
  13. 13.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60(1-4), 259–268 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Bruhn, A., Weickert, J., Schnörr, C.: Lucas/kanade meets horn/schunck: Combining local and global optic flow methods. Int. J. Comput. Vision 61(3), 211–231 (2005)CrossRefGoogle Scholar
  15. 15.
    Bruhn, A., Weickert, J., Feddern, C., Kohlberger, T., Schnörr, C.: Variational optic flow computation in real-time. IEEE Trans. Image Proc. 14(5), 608–615 (2005)CrossRefzbMATHGoogle Scholar
  16. 16.
    Bruhn, A., Weickert, J., Kohlberger, T., Schnörr, C.: Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds.) Scale-Space 2005. LNCS, vol. 3459, pp. 279–290. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Bruhn, A., Weickert, J., Kohlberger, T., Schnörr, C.: A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. Int. J. Comput. Vision 70(3), 257–277 (2006)CrossRefGoogle Scholar
  18. 18.
    Lei, C., Yang, Y.-H.: Optical flow estimation on coarse-to-fine region-trees using discrete optimization. In: ICCV, pp. 1562–1569 (2009)Google Scholar
  19. 19.
    Lee, K.J., Kwon, D., Yun, I.D., Lee, S.U.: Optical flow estimation with adaptive convolution kernel prior on discrete framework. In: CVPR, pp. 2504–2511. IEEE (2010)Google Scholar
  20. 20.
    Lempitsky, V.S., Roth, S., Rother, C.: Fusionflow: Discrete-continuous optimization for optical flow estimation. In: CVPR (2008)Google Scholar
  21. 21.
    Glocker, B., Paragios, N., Komodakis, N., Tziritas, G., Navab, N.: Optical flow estimation with uncertainties through dynamic mrfs. In: CVPR (2008)Google Scholar
  22. 22.
    Seitz, S.M., Baker, S.: Filter flow. In: ICCV, pp. 143–150 (2009)Google Scholar
  23. 23.
    Sun, D., Roth, S., Lewis, J.P., Black, M.J.: Learning Optical Flow. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 83–97. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Li, Y., Huttenlocher, D.P.: Learning for Optical Flow Using Stochastic Optimization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 379–391. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Duc Dung Nguyen
    • 1
  • Jae Wook Jeon
    • 1
  1. 1.Department of Electrical and Computer EngineeringSungkyunkwan UniversityKorea

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