| OpenCV 4.3.0 Open Source Computer Vision

Classes | Namespaces | Functions
pcaflow.hpp File Reference

Implementation of the PCAFlow algorithm from the following paper: http://files.is.tue.mpg.de/black/papers/cvpr2015_pcaflow.pdf. More...

`#include "opencv2/core.hpp"`
`#include "opencv2/video.hpp"`

## Classes
class | cv::optflow::OpticalFlowPCAFlow
| PCAFlow algorithm. More...
class | cv::optflow::PCAPrior
| This class can be used for imposing a learned prior on the resulting optical flow. Solution will be regularized according to this prior. You need to generate appropriate prior file with "learn_prior.py" script beforehand. More...

## Namespaces
| cv
| "black box" representation of the file storage associated with a file on disk.
| cv::optflow

## Functions
Ptr< DenseOpticalFlow > | cv::optflow::createOptFlow_PCAFlow ()
| Creates an instance of PCAFlow. More...

## Detailed Description

Implementation of the PCAFlow algorithm from the following paper: http://files.is.tue.mpg.de/black/papers/cvpr2015_pcaflow.pdf.

Author
  Vladislav Samsonov vvlad.nosp@m.xx@g.nosp@m.mail..nosp@m.com [261]

There are some key differences which distinguish this algorithm from the original PCAFlow (see paper):

- Discrete Cosine Transform basis is used instead of basis extracted with PCA. Reasoning: DCT basis has comparable performance and it doesn't require additional storage space. Also, this decision helps to avoid overloading the algorithm with a lot of external input.
- Usage of built-in OpenCV feature tracking instead of libviso.