Optical Flow Estimation
444 papers with code • 9 benchmarks • 29 datasets
Optical Flow Estimation is the problem of finding pixel-wise motions between consecutive images.
Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.
Definition source: Devon: Deformable Volume Network for Learning Optical Flow
Image credit: Optical Flow Estimation
It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow.
RAFT extracts per-pixel features, builds multi-scale 4D correlation volumes for all pairs of pixels, and iteratively updates a flow field through a recurrent unit that performs lookups on the correlation volumes.
A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible.
Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art.
Our method develops a deep fully convolutional neural network that takes two input frames and estimates pairs of 1D kernels for all pixels simultaneously.