In the last decades variational optical flow algorithms have been intensively studied by the computer vision community. However, relatively few effort has been made to obtain robust confidence measures for the estimated flow field. As many applications do not require the whole flow field, it would be helpful to identify the parts of the field where the flow is most accurate. We propse a confidence measure based on the energy functional that is minimized during the optical flow calculation and analyze the performance of different data terms. For evaluation, 7 datasets of the Middlebury benchmark are used. The results show that the accuracy of the flow field can be improved by 53.3% if points are selected according to the proposed confidence measure. The suggested method leads to an improvement of 35.2% compared to classical confidence measures.
