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ProFlow: Learning to Predict Optical Flow
[article]
2018
arXiv
pre-print
Temporal coherence is a valuable source of information in the context of optical flow estimation. However, finding a suitable motion model to leverage this information is a non-trivial task. In this paper we propose an unsupervised online learning approach based on a convolutional neural network (CNN) that estimates such a motion model individually for each frame. By relating forward and backward motion these learned models not only allow to infer valuable motion information based on the
arXiv:1806.00800v1
fatcat:p6tk5bmm5vbpxl65w5rlwkcinu