Abstract

In this paper a novel approach for estimating the three di- mensional motion field of the visible world from stereo image sequences is proposed. This approach combines dense variational optical flow estima- tion, including spatial regularization, with Kalman filtering for temporal smoothness and robustness. The result is a dense, robust, and accurate reconstruction of the three-dimensional motion field of the current scene that is computed in real-time. Parallel implementation on a GPU and an FPGA yields a vision-system which is directly applicable in real- world scenarios, like automotive driver assistance systems or in the field of surveillance. Within this paper we systematically show that the pro- posed algorithm is physically motivated and that it outperforms existing approaches with respect to computation time and accuracy.