Optical Flow

Abstract—The zero-mean normalized cross-correlation was shown to improve the accuracy of optical flow, but it’s analytical form is quite complicated for the variational framework.  This work addresses this issue and presents a new direct approach to this matching measure. Our approach uses the correlation transform to define very discriminative descriptors that are pre-computed and that have to be matched in the target frame. It is equivalent to the computation of the optical flow for the correlation transforms of the images. The smoothness energy is non-local and uses a robust penalty in order to preserve motion discontinuities. The model is associated with a fast and parallelizable minimization procedure based on the projected-proximal point algorithm. The experiments confirm the strength of this model and implicitly demonstrate the correctness of our solution. The results demonstrate that the involved data term is very robust with respect to changes in illumination, especially where large illumination exists. 

Abstract— More data fidelity terms in variational optical flow methods improve the estimation’s robustness. A robust and anisotropic smoother enhances the specific fill-in process.  This work presents a combined local-global (CLG) approach with total variation regularization. The combination of bilateral filtering and anisotropic (image driven) regularization is used to control the propagation phenomena. The resulted method, CLG-TV, is able to compute larger displacements in a reasonable time. The numerical scheme is highly parallelizable and runs in real-time on current generation graphic processing units.

Article:

M. Drulea and S. Nedevschi, "Total variation regularization of local-global optical flow," in Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, 2011, pp. 318-323