Illumination invariance for driving scene optical flow using comparagram preselection

In the recent years, advanced video sensors have become common in driver assistance, coping with the highly dynamic lighting conditions by nonlinear exposure adjustments. However, many computer vision algorithms are still highly sensitive to the resulting sudden brightness changes. We present a method that is able to estimate the relative intensity transfer function (RITF) between images in a sequence even for moving cameras. The according compensation of the input images can improve the performance of further vision tasks significantly, here demonstrated by results from optical flow. Our method identifies corresponding intensity values from areas in the images where no apparent motion is present. The RITF is then estimated from that data and regularized based on its curvature. Finally, built-in tests reliably flag image pairs with `adverse conditions´ where no compensation could be performed.