An Unbiased Second-Order Prior for High-Accuracy Motion Estimation

Abstract. Virtually all variational methods for motion estimation regularize the gradient of the flow field, which introduces a bias towards piecewise constant motions in weakly textured areas. We propose a novel regularization approach, based on decorrelated second-order derivatives, that does not suffer from this shortcoming. We then derive an efficient numerical scheme to solve the new model using projected gradient descent. A comparison to a TV regularized model shows that the proposed second-order prior exhibits superior performance, in particular in lowtextured areas (where the prior becomes important). Finally, we show that the proposed model yields state-of-the-art results on the Middlebury optical flow database.

• Type: Conference
• Year: 2008
• Where: DAGM
• Authors: Werner Trobin, Thomas Pock, Daniel Cremers, Horst Bischof
• Keywords: DAGM 2008, Decorrelated Second-order Derivatives, Image Processing, Piecewise Constant Motions, Second-order Prior Exhibits