Abstract
A look at the Middlebury optical flow benchmark [5] reveals that nowadays variational methods yield the most accurate optical flow fields between two image frames. In this work we propose an improvement variant of the original duality based TV-L 1 optical flow algorithm in [31] and provide implementation details. This formulation can preserve discontinuities in the flow field by employing total variation (TV) regularization. Furthermore, it offers robustness against outliers by applying the robust L 1 norm in the data fidelity term.
Our contributions are as follows. First, we propose to perform a structure-texture decomposition of the input images to get rid of violations in the optical flow constraint due to illumination changes. Second, we propose to integrate a median filter into the numerical scheme to further increase the robustness to sampling artefacts in the image data. We experimentally show that very precise and robust estimation of optical flow can be achieved with a variational approach in real-time. The numerical scheme and the implementation are described in a detailed way, which enables reimplementation of this high-end method.
Keywords
- Input Image
- Optical Flow
- Outer Iteration
- Illumination Change
- Improve Algorithm
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.
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References
Alvarez, L., Weickert, J., Sánchez, J.: A scale-space approach to nonlocal optical flow calculations. In: Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision, pp. 235–246 (1999)
Anandan, P.: A computational framework and an algorithm for the measurement of visual motion. Int. J. Comput. Vision 2, 283–310 (1989)
Aubert, G., Deriche, R., Kornprobst, P.: Computing optical flow via variational techniques. SIAM J. Appl. Math. 60(1), 156–182 (1999)
Aujol, J.-F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition–modeling, algorithms, and parameter selection. Int. J. Comput. Vision 67(1), 111–136 (2006)
Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A Database and Evaluation Methodology for Optical Flow. In: ICCV 1993, pp. 1–8 (2007)
Birchfield, S.: Derivation of Kanade-Lucas-Tomasi Tracking Equation. Technical Report (1997)
Black, M.J., Anandan, P.: A framework for the robust estimation of optical flow. In: ICCV 1993, pp. 231–236 (1993)
Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J., Osher, S.: Fast Global Minimization of the Active Contour/Snake Model. Journal of Mathematical Imaging and Vision (2007)
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)
Bruhn, A., et al.: Variational optical flow computation in real time. IEEE Transactions on Image Processing 14(5), 608–615 (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)
Camus, T.A.: Real-time quantized optical flow. Journal of Real-Time Imaging 3, 71–86 (1997); Special Issue on Real-Time Motion Analysis
Chambolle, A.: An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision 20(1–2), 89–97 (2004)
Chambolle, A.: Total variation minimization and a class of binary MRF models. Energy Minimization Methods in Computer Vision and Pattern Recognition, 136–152 (2005)
Chan, T.F., Golub, G.H., Mulet, P.: A nonlinear primal-dual method for total variation-based image restoration. In: ICAOS 1996, Paris, vol. 219, pp. 241–252 (1996)
Devillard, N.: Fast median search: an ANSI C implementation (1998), http://www.eso.org/ndevilla/median/ (visited October 2008)
Haussecker, H., Fleet, D.J.: Estimating Optical Flow with Physical Models of Brightness Variation. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 661–674 (2001)
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)
Mémin, E., Pérez, P.: Hierarchical estimation and segmentation of dense motion fields. Int. J. Comput. Vision 46(2), 129–155 (2002)
Mileva, Y., Bruhn, A., Weickert, J.: Illumination-robust Variational Optical Flow with Photometric Invariants. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 152–162. Springer, Heidelberg (2007)
Nagel, H.-H., Enkelmann, W.: An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 8, 565–593 (1986)
Papenberg, N., Bruhn, A., Brox, T., Didas, S., Weickert, J.: Highly accurate optic flow computation with theoretically justified warping. Int. J. Comput. Vision, 141–158 (2006)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Stewart, E.: Intel Integrated Performance Primitives: How to Optimize Software Applications Using Intel IPP. Intel Press (2004) ISBN 0971786135
Strzodka, R., Garbe, C.: Real-time motion estimation and visualization on graphics cards. In: IEEE Visualization 2004, pp. 545–552 (2004)
Trobin, W., Pock, T., Cremers, D., Bischof, H.: An Unbiased Second-Order Prior for High-Accuracy Motion Estimation. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 396–405. Springer, Heidelberg (2008)
Wedel, A., Pock, T., Braun, J., Franke, U., Cremers, D.: Duality TV-L1 flow with fundamental matrix prior. Image and Vision Computing New Zealand (November 2008)
Wedel, A., Rabe, C., Vaudrey, T., Brox, T., Franke, U., Cremers, D.: Efficient Dense Scene Flow from Sparse or Dense Stereo Data. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 739–751. Springer, Heidelberg (2008)
Weickert, J., Brox, T.: Diffusion and regularization of vector- and matrix-valued images. Inverse Problems, Image Analysis and Medical Imaging. Contemporary Mathematics 313, 251–268 (2002)
van de Weijer, J., Gevers, T.: Robust Pptical Flow from Photometric Invariants. In: International Conference on Image Processing (2004)
Zach, C., Pock, T., Bischof, H.: A Duality Based Approach for Realtime TV-L1 Optical Flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214–223. Springer, Heidelberg (2007)
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Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D. (2009). An Improved Algorithm for TV-L 1 Optical Flow. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds) Statistical and Geometrical Approaches to Visual Motion Analysis. Lecture Notes in Computer Science, vol 5604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03061-1_2
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DOI: https://doi.org/10.1007/978-3-642-03061-1_2
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