Block-matching based optical flow estimation with reduced search space based on geometric constraints

In this paper we propose a new block-matching based approach for the estimation of nearly dense optical flow fields in image sequences. We focus on applications to autonomous vehicles where a dominant movement of the camera along its optical axis is present. The presented algorithm exploits the geometric relations between the two viewpoints induced by the epipolar geometry, hence it is applicable for the static parts of the scene. These relations are used to remap the images so that the resulting virtual images are similar to images captured by an axial stereo camera setup. This alignment dramatically reduces the computational complexity of the correspondence search and avoids false correspondences e.g. caused by repeated patterns. Experiments on challenging real-world sequences show the accuracy of the proposed approach.