Clemens Rabe, Thomas Müller, Andreas Wedel, Uwe FrankeAbstractIn this paper a novel approach for
estimating the three dimensional motion field of the visible world from
stereo image sequences is proposed. This approach combines dense
variational optical flow estimation, 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 proposed algorithm is
physically motivated and that it outperforms existing approaches with
respect to computation time and accuracy. [Download] [View] [BibTeX] |
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