Abstract
Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).
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References
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision (DARPA). In: DARPA Image Understanding Workshop, pp. 121–130 (April 1981)
Horn, B.K.P., Schunk, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)
Bruhn, A.: Variational Optic Flow Computation: Accurate Modelling and Efficient Numerics. PhD thesis, Department of Mathematics and Computer Science, Saarland University, Saarbrücken (2006)
Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–77 (1994)
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)
Wedel, A., Pock, T., Zach, C., Cremers, D., Bischof, H.: An improved algorithm for TV-L1 optical flow. In: Dagstuhl Motion Workshop, Dagstuhl Castle, Germany, pp. 23–45 (September 2008)
Weickert, J., Schnörr, C.: A theoretical framework for convex regularizers in pde-based computation of image motion. Internal Journal of Computer Vision 45(3), 245–264 (2001)
Wedel, A., Cremers, D., Pock, T., Bischof, H.: Structure- and motion-adaptive regularization for high accuracy optic flow. In: IEEE International Conference of Computer Vision, Kyoto, Japan, pp. 1663–1668 (2009)
Zimmer, H., Bruhn, A., Weickert, J.: Optic flow in harmony. Internal Journal of Computer Vision 93(3), 368–388 (2011)
Steinbruecker, F., Pock, T., Cremers, D.: Advanced data terms for variational optic flow estimation. In: Vision, Modeling, and Visualization Workshop, Braunschweig, Germany, pp. 155–164 (2009)
Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: IEEE Conference of Computer Vision and Pattern Recognition, San Francisco, CA, USA, pp. 2432–2439 (June 2010)
Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. In: IEEE International Conference of Computer Vision, pp. 1–8 (October 2007)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: Computer Vision and Pattern Recognition (CVPR), Providence, USA (June 2012)
http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=flow
Valgaerts, L., Bruhn, A., Weickert, J.: A variational model for the joint recovery of the fundamental matrix and the optical flow. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 314–324. Springer, Heidelberg (2008)
Onkarappa, N., Sappa, A.D.: An empirical study on optical flow accuracy depending on vehicle speed. In: IEEE Intelligent Vehicles Symposium, pp. 1138–1143 (June 2012)
Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L 1 optical flow. In: DAGM Symposium, Heidelberg, Germany, pp. 214–223 (September 2007)
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Onkarappa, N., Sappa, A.D. (2013). Laplacian Derivative Based Regularization for Optical Flow Estimation in Driving Scenario. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_60
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DOI: https://doi.org/10.1007/978-3-642-40246-3_60
Publisher Name: Springer, Berlin, Heidelberg
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