Optic Flow: Improving Discontinuity Preserving
The aim of this paper is to analyze the discontinuity preserving behavior of two methods in optical flow. With this objetive, we have implemented a well-known optical flow method that uses isotropic TV-L 1 regularization. For the second approach, we have modified this method, by adding a decreasing function in the regularization term, to avoid smoothing at flow discontinuities. As a consequence, we see a high improvement and a very accurate discontinuities detection in some sequences but not good enough in others. Adapting the weight of the decreasing function allows us to better define the flow discontinuities. Nevertheless, the experimental results show that the parameters that yield a good segmentation of the motion field, may also introduce important unstabilities. In this sense, the results seem promising, but it is very difficult to set a unified parameter configuration that works fine for all the sequences. We evaluate the performance of these approaches with some standard test sequences, such as the Middlebury benchmark database or the Yosemite sequence. Looking for the best parameters configuration, which provides the best contour definition, does not typically mean a solution which is closer to the ground truth.
KeywordsOptical Flow Discontinuity Preserving TV-L1 Variational Methods Isotropic Regularization
Unable to display preview. Download preview PDF.
- 1.Álvarez, L., Esclarín, J., Lefébure, M., Sánchez, J.: A pde model for computing the optical flow. In: XVI Congreso de Ecuaciones Diferenciales y Aplicaciones, C.E.D.Y.A. XVI, Las Palmas de Gran Canaria, Spain, pp. 1349–1356 (1999)Google Scholar
- 7.Sánchez, J., Monzón, N., Salgado, A.: Robust Optical Flow Estimation. Image Processing On Line 2013, 242–260 (2013)Google Scholar
- 8.Wedel, A., Cremers, D., Pock, T., Bischof, H.: Structure- and motion-adaptive regularization for high accuracy optic flow. In: IEEE International Conference on Computer Vision, pp. 1663–1668 (September 2009)Google Scholar
- 9.Xu, L., Jia, J., Matsushita, Y.: Motion detail preserving optical flow estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1293–1300 (June 2010)Google Scholar