Abstract
The brightness constancy assumption has widely been used in variational optical flow approaches as their basic foundation. Unfortunately, this assumption does not hold when illumination changes or for objects that move into a part of the scene with different brightness conditions. This paper proposes a variation of the L1-norm dual total variational (TV-L1) optical flow model with a new illumination-robust data term defined from the histogram of oriented gradients computed from two consecutive frames. In addition, a weighted non-local term is utilized for denoising the resulting flow field. Experiments with complex textured images belonging to different scenarios show results comparable to state-of-the-art optical flow models, although being significantly more robust to illumination changes.
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References
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Brox, T., Malik, J.: Large displacement optical flow: Descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(3), 500–513 (2011)
Bruhn, A., Weickert, J.: Towards ultimate motion estimation: Combining highest accuracy with real-time performance. In: ICCV, pp. 749–755. IEEE Computer Society (2005)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (1), pp. 886–893. IEEE Computer Society (2005)
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artifical Intelligence 17(1-3), 185–203 (1981)
Kim, Y.H., Martínez, A.M., Kak, A.C.: Robust motion estimation under varying illumination. Image Vision Comput. 23(4), 365–375 (2005)
Liu, C., Yuen, J., Torralba, A.: Sift flow: Dense correspondence across scenes and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(5), 978–994 (2011)
Mattavelli, M., Nicoulin, A.N.: Motion estimation relaxing the constancy brightness constraint. In: ICIP, pp. 770–774. IEEE (1994)
Molnar, J., Chetverikov, D., Fazekas, S.: Illumination-robust variational optical flow using cross-correlation. Computer Vision and Image Understanding 114(10), 1104–1114 (2010)
Müller, T., Rabe, C., Rannacher, J., Franke, U., Mester, R.: Illumination-robust dense optical flow using census signatures. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 236–245. Springer, Heidelberg (2011)
Pock, T., Urschler, M., Zach, C., Beichel, R., Bischof, H.: A duality based algorithm for TV-L 1-optical-flow image registration. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 511–518. Springer, Heidelberg (2007)
Rudin, L.I., Osher, S.J., Fatemi, E.: Nonlinear total variation based noise removal algorithms 60, 259–268 (1992), http://dx.doi.org/10.1016/0167-2789(92)90242-F
Sand, P., Teller, S.J.: Particle video: Long-range motion estimation using point trajectories. International Journal of Computer Vision 80(1), 72–91 (2008)
Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: CVPR, pp. 2432–2439. IEEE (2010)
Werlberger, M., Pock, T., Bischof, H.: Motion estimation with non-local total variation regularization. In: CVPR, pp. 2464–2471. IEEE (2010)
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)
Zimmer, H., Bruhn, A., Weickert, J.: Optic flow in harmony. International Journal of Computer Vision 93(3), 368–388 (2011)
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Rashwan, H.A., Mohamed, M.A., García, M.A., Mertsching, B., Puig, D. (2013). Illumination Robust Optical Flow Model Based on Histogram of Oriented Gradients. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_38
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DOI: https://doi.org/10.1007/978-3-642-40602-7_38
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