Dense, Robust, and Accurate Motion Field Estimation from Stereo Image Sequences in Real-Time
Abstract
In 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.
Cite
Text
Rabe et al. "Dense, Robust, and Accurate Motion Field Estimation from Stereo Image Sequences in Real-Time." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15561-1_42Markdown
[Rabe et al. "Dense, Robust, and Accurate Motion Field Estimation from Stereo Image Sequences in Real-Time." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/rabe2010eccv-dense/) doi:10.1007/978-3-642-15561-1_42BibTeX
@inproceedings{rabe2010eccv-dense,
title = {{Dense, Robust, and Accurate Motion Field Estimation from Stereo Image Sequences in Real-Time}},
author = {Rabe, Clemens and Müller, Thomas and Wedel, Andreas and Franke, Uwe},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {582-595},
doi = {10.1007/978-3-642-15561-1_42},
url = {https://mlanthology.org/eccv/2010/rabe2010eccv-dense/}
}