Feature- and depth-supported modified total variation optical flow for 3D motion field estimation in real scenes

We propose and evaluate improvements in motion field estimation in order to cope with challenges in real world scenarios. To build a real-time stereo-based three-dimensional vision system which is able to handle illumination changes, textureless regions and fast moving objects observed by a moving platform, we introduce a new approach to support the variational optical flow computation scheme with stereo and feature information. The improved flow result is then used as input for a temporal integrated robust three-dimensional motion field estimation technique. We evaluate the results of our optical flow algorithm and the resulting three-dimensional motion field against approaches known from literature. Tests on both synthetic realistic and real stereo sequences show that our approach is superior to approaches known from literature with respect to density, accuracy and robustness.