Trinocular optical flow estimation for intelligent vehicle applications

Motion is an important clue for many tasks in visual scene perception. In this paper, we present a new matching-based algorithm to estimate nearly dense optical flow fields for the static parts of the scene, i.e. those parts whose motion is induced by the moving observer only. Our algorithm is designed for applications in intelligent vehicles usually equipped with stereo camera rigs. To address the computational effort of matching-based approaches we use constraints arising from the geometry between multiple views. To this end, we compute both an approximated optical flow field and an approximated disparity field between left and right image. Hence, we can predict the position of the corresponding candidate and limit the search space to a small neighborhood around the predicted position leading to near real-time capabilities. Experiments on different challenging real world images show the accuracy and efficiency of the proposed approach.