Abstract
In this chapter an approach for estimating the three-dimensional motion fields of real-world scenes is proposed. This approach combines state-of-the-art dense optical flow estimation, including spatial regularization, and dense stereo information using Kalman filters to achieve temporal smoothness and robustness. The result is a dense and accurate reconstruction of the three-dimensional motion field of the observed scene. An efficient parallel implementation using a GPU and an automotive compliant FPGA yields a real-time vision system which is directly applicable in real-world scenarios including driver assistance systems, robotics, and surveillance.
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Rabe, C., Franke, U., Koch, R. (2014). Dense 3D Motion Field Estimation from a Moving Observer in Real Time. In: Schmidt, G., Abut, H., Takeda, K., Hansen, J. (eds) Smart Mobile In-Vehicle Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9120-0_2
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