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JeeHang Lee

Blur Robust Optical Flow using Motion Channel

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Mar 07, 2016
Wenbin Li, Yang Chen, JeeHang Lee, Gang Ren, Darren Cosker

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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.

* Preprint of our paper accepted by Neurocomputing 
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