The impact of nonlinear filtering and confidence information on optical flow estimation in a Lucas & Kanade framework
Determining optical flow has been a wide field of research for more than 20 years now that has not been solved satisfactorily yet. In this work, we study the influence of a nonlinear smoothing process based on bilateral filtering on a Lucas & Kanade framework for the estimation of optical flow between two image frames. Different confidence measures are used to improve the computation process and detect occlusion and innovation phenomena, explicitly handling discontinuous flow fields. By means of simulations we report that the accuracy can be increased significantly, making this approach interesting for further investigations.