computer-vision-11
Optical Flow Estimation
3260 papers • 126 benchmarks • 313 datasets
Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression. Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based. Further readings: Optical Flow Estimation Performance of Optical Flow Techniques Definition source: Devon: Deformable Volume Network for Learning Optical Flow Image credit: Optical Flow Estimation
(Image credit: Papersgraph)
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PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Ming-Yu Liu, Jan Kautz, Xiaodong Yang, Deqing Sun•Wed Sep 06 2017
PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume, and outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks.
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