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.
Chapter PDF
Similar content being viewed by others
Keywords
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
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17(1-3), 185–203 (1981)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI, pp. 674–679 (April 1981)
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)
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)
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)
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)
Werlberger, M., Pock, T., Bischof, H.: Motion estimation with non-local total variation regularization. In: CVPR, pp. 2464–2471 (2010)
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)
Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic huber-l1 optical flow. In: BMVC, British Machine Vision Association (2009)
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)
Bruhn, A., Weickert, J.: Towards ultimate motion estimation: combining highest accuracy with real-time performance. In: ICCV, vol. 1, pp. 749–755 (October 2005)
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)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60(1-4), 259–268 (1992)
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)
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)
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)
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)
Lei, C., Yang, Y.-H.: Optical flow estimation on coarse-to-fine region-trees using discrete optimization. In: ICCV, pp. 1562–1569 (2009)
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)
Lempitsky, V.S., Roth, S., Rother, C.: Fusionflow: Discrete-continuous optimization for optical flow estimation. In: CVPR (2008)
Glocker, B., Paragios, N., Komodakis, N., Tziritas, G., Navab, N.: Optical flow estimation with uncertainties through dynamic mrfs. In: CVPR (2008)
Seitz, S.M., Baker, S.: Filter flow. In: ICCV, pp. 143–150 (2009)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nguyen, D.D., Jeon, J.W. (2011). Improving Motion Estimation Using Image-Driven Functions and Hybrid Scheme. In: Ho, YS. (eds) Advances in Image and Video Technology. PSIVT 2011. Lecture Notes in Computer Science, vol 7087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25367-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-25367-6_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25366-9
Online ISBN: 978-3-642-25367-6
eBook Packages: Computer ScienceComputer Science (R0)