Bootstrap optical flow confidence and uncertainty measure

We address the problem of estimating the uncertainty of optical flow algorithm results. Our method estimates the error magnitude at all points in the image. It can be used as a confidence measure. It is based on bootstrap resampling, which is a computational statistical inference technique based on repeating the optical flow calculation several times for different randomly chosen subsets of pixel contributions. As few as ten repetitions are enough to obtain useful estimates of geometrical and angular errors. For demonstration, we use the combined local–global optical flow method (CLG) which generalizes both Lucas–Kanade and Horn–Schunck type methods. However, the bootstrap method is very general and can be applied to almost any optical flow algorithm that can be formulated as a pixel-based minimization problem. We show experimentally on synthetic as well as real video sequences with known ground truth that the bootstrap method performs better than all other confidence measures tested.

► We estimate the uncertainty of optical flow computation from the input images only.
► Bootstrap resampling calculates the OF repeatedly for different pixel subsets.
► We use the combined local–global OF method but the technique is general.
► The bootstrap method performs better than all other confidence measures tested.