Robust Optical Flow Estimation for Continuous Blurred Scenes Using RGB-Motion Imaging and Directional Filtering
Abstract
Optical flow estimation is a difficult task given real-world video footage with camera and object blur. In this paper, we combine a 3D pose&position tracker with an RGB sensor allowing us to capture video footage together with 3D camera motion. We show that the additional camera motion information can be embedded into a hybrid optical flow framework by interleaving an iterative blind deconvolution and warping based minimization scheme. Such a hybrid framework significantly improves the accuracy of optical flow estimation in scenes with strong blur. Our approach yields improved overall performance against three state-of-the-art baseline methods applied to our proposed ground truth sequences, as well as in several other real-world sequences captured by our novel imaging system.
Cite
Text
Li et al. "Robust Optical Flow Estimation for Continuous Blurred Scenes Using RGB-Motion Imaging and Directional Filtering." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836022Markdown
[Li et al. "Robust Optical Flow Estimation for Continuous Blurred Scenes Using RGB-Motion Imaging and Directional Filtering." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/li2014wacv-robust/) doi:10.1109/WACV.2014.6836022BibTeX
@inproceedings{li2014wacv-robust,
title = {{Robust Optical Flow Estimation for Continuous Blurred Scenes Using RGB-Motion Imaging and Directional Filtering}},
author = {Li, Wenbin and Chen, Yang and Lee, JeeHang and Ren, Gang and Cosker, Darren},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {792-799},
doi = {10.1109/WACV.2014.6836022},
url = {https://mlanthology.org/wacv/2014/li2014wacv-robust/}
}