It is hard to estimate optical flow given a realworld video sequence with camera shake and other motion blur. In this paper, we first investigate the blur parameterization for video footage using near linear motion elements. we then combine a commercial 3D pose sensor with an RGB camera, in order to film video footage of interest together with the camera motion. We illustrates that this additional camera motion/trajectory channel can be embedded into a hybrid framework by interleaving an iterative blind deconvolution and warping based optical flow scheme. Our method yields improved accuracy within three other state-of-the-art baselines given our proposed ground truth blurry sequences; and several other realworld sequences filmed by our imaging system.
@article{arxiv.1603.02253,
title = {Blur Robust Optical Flow using Motion Channel},
author = {Wenbin Li and Yang Chen and JeeHang Lee and Gang Ren and Darren Cosker},
journal= {arXiv preprint arXiv:1603.02253},
year = {2016}
}