Computes a dense optical flow using the Gunnar Farneback’s algorithm.
farneback(image1, image2, pyr_scale = 0.5, levels = 3, winsize = 43,
iterations = 3, poly_n = 7, poly_sigma = 1.5)| image1 | An |
|---|---|
| image2 | An |
| pyr_scale | Parameter, specifying the image scale (<1) to build pyramids for each image; pyr_scale = 0.5 means a classical pyramid, where each next layer is twice smaller than the previous one. |
| levels | Number of pyramid layers including the initial image; levels = 1 means that no extra layers are created and only the original images are used. |
| winsize | Averaging window size; larger values increase the algorithm robustness to image noise and give more chances for fast motion detection, but yield more blurred motion field. |
| iterations | Number of iterations the algorithm does at each pyramid level. |
| poly_n | Size of the pixel neighborhood used to find polynomial expansion in each pixel; larger values mean that the image will be approximated with smoother surfaces, yielding more robust algorithm and more blurred motion field, typically poly_n = 5 or 7. |
| poly_sigma | Standard deviation of the Gaussian that is used to smooth derivatives used as a basis for the polynomial expansion; for poly_n = 5, you can set poly_sigma = 1.1, for poly_n = 7, a good value would be poly_sigma = 1.5. |
A matrix with the same number of rows and columns as the original images, and two layers representing the x and y components of the optical flow for each pixel of the image.
Farnebäck G. Two-Frame Motion Estimation Based on Polynomial Expansion. In: Bigun J, Gustavsson T, editors. Image Analysis. Springer Berlin Heidelberg; 2003. pp. 363–370. doi:10.1007/3-540-45103-X_50
Simon Garnier, garnier@njit.edu
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