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I'm going through Farneback optical flow estimation and, in particular, through the following line

cuda::FarnebackOpticalFlow::create(int numLevels=5, double pyrScale=0.5, bool fastPyramids=false, int winSize=13, int numIters=10, int polyN=5, double polySigma=1.1, int flags=0)

creating the Farneback estimator. It seem I could not find a comprehensive documentation for the input parameters. Although I understand most of them, two are still not clear to me:

bool fastPyramids

and

double polySigma

Concerning the former, what is the fast pyramids approach? Concerning the latter, I have found that

polySigma is the standard deviation of the Gaussian that is used to smooth derivatives used as a basis for the polynomial expansion; for polyN=5, you can set polySigma=1.1, for polyN=7, a good value would be polySigma=1.5.

In which way are we smoothing derivatives?

Thank you for any help.

I'm going through Farneback optical flow estimation and, in particular, through the following line

cuda::FarnebackOpticalFlow::create(int numLevels=5, double pyrScale=0.5, bool fastPyramids=false, int winSize=13, int numIters=10, int polyN=5, double polySigma=1.1, int flags=0)

creating the Farneback estimator. It seem I could not find a comprehensive documentation for the input parameters. Although I understand most of them, two are still not clear to me:

bool fastPyramids

and

double polySigma

Concerning the former, what is the fast pyramids approach? Concerning the latter, I have found that

polySigma is the standard deviation of the Gaussian that is used to smooth derivatives used as a basis for the polynomial expansion; for polyN=5, you can set polySigma=1.1, for polyN=7, a good value would be polySigma=1.5.

In which way are we smoothing derivatives?

Thank you for any help.