Object for estimating optical flow using Farneback method
Create an optical flow object for estimating the direction and speed of moving
objects using the Farneback method. Use the object function
estimateFlow to estimate the optical flow vectors. Using the
reset object function, you can reset the internal state of the optical
returns an optical flow object that you can use to estimate the direction and
speed of the moving objects in a video. The optical flow is estimated using the
opticFlow = opticalFlowFarneback
returns an optical flow object with properties specified as one or more
opticFlow = opticalFlowFarneback(
Name,Value pair arguments. Any unspecified properties
have default values. Enclose each property name in quotes.
NumPyramidLevels — Number of pyramid layers
3 (default) | positive scalar
Number of pyramid layers, specified as a positive scalar. The value
includes the initial image as one of the layers. When you set this value to
1, the function estimates optical flow only from the
original image frame and does not perform pyramid decomposition. The
recommended values are between
PyramidScale — Image scale
0.5 (default) | positive scalar in the range (0,1)
Image scale, specified as a positive scalar in the range (0,1). The value
specifies the rate of downsampling at each pyramid level. A value of
0.5 creates a classical pyramid, where the resolution
of the pyramid reduces by a factor of two at each level. The lowest level in
the pyramid has the highest resolution.
NumIterations — Number of search iterations per pyramid level
3 (default) | positive integer
Number of search iterations per pyramid level, specified as a positive integer. The Farneback algorithm performs an iterative search for the key points at each pyramid level, until convergence.
NeighborhoodSize — Size of the pixel neighborhood
5 (default) | positive integer
Size of the pixel neighborhood, specified as a positive integer. Increase
the neighborhood size to increase blurred motion. The blur motion yields a
more robust estimation of optical flow. A typical value for
FilterSize — Averaging filter size
15 (default) | positive integer in the range [2, Inf)
Averaging filter size, specified as a positive integer in the range [2,
Inf). After the algorithm computes the displacement (flow), the averaging
over neighborhoods is done using a Gaussian filter of size
Also, the pixels close to the borders are given a reduced weight because the
algorithm assumes that the polynomial expansion coefficients are less
reliable there. Increasing the filter size increases the robustness of the
algorithm to image noise. The larger the filter size, the greater the
algorithm handles image noise and fast motion detection, making it more
|Estimate optical flow|
|Reset the internal state of the optical flow estimation object|
Estimate Optical Flow Using Farneback Method
Read a video file. Specify the timestamp of the frame to be read.
vidReader = VideoReader('visiontraffic.avi','CurrentTime',11);
Create an optical flow object for estimating the optical flow using Farneback method. The output is an object specifying the optical flow estimation method and its properties.
opticFlow = opticalFlowFarneback
opticFlow = opticalFlowFarneback with properties: NumPyramidLevels: 3 PyramidScale: 0.5000 NumIterations: 3 NeighborhoodSize: 5 FilterSize: 15
Create a custom figure window to visualize the optical flow vectors.
h = figure; movegui(h); hViewPanel = uipanel(h,'Position',[0 0 1 1],'Title','Plot of Optical Flow Vectors'); hPlot = axes(hViewPanel);
Read the image frames and convert to grayscale images. Estimate the optical flow from consecutive image frames. Display the current image frame and plot the optical flow vectors as quiver plot.
while hasFrame(vidReader) frameRGB = readFrame(vidReader); frameGray = im2gray(frameRGB); flow = estimateFlow(opticFlow,frameGray); imshow(frameRGB) hold on plot(flow,'DecimationFactor',[5 5],'ScaleFactor',2,'Parent',hPlot); hold off pause(10^-3) end
The Farneback algorithm generates an image pyramid, where each level has a lower resolution compared to the previous level. When you select a pyramid level greater than 1, the algorithm can track the points at multiple levels of resolution, starting at the lowest level. Increasing the number of pyramid levels enables the algorithm to handle larger displacements of points between frames. However, the number of computations also increases. The diagram shows an image pyramid with three levels.
The tracking begins in the lowest resolution level, and continues until convergence. The point locations detected at a level are propagated as keypoints for the succeeding level. In this way, the algorithm refines the tracking with each level. The pyramid decomposition enables the algorithm to handle large pixel motions, which can be distances greater than the neighborhood size.
 Farneback, G. “Two-Frame Motion Estimation Based on Polynomial Expansion.” In Proceedings of the 13th Scandinavian Conference on Image Analysis, 363 - 370. Halmstad, Sweden: SCIA, 2003.
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
Generates portable C code using a C++ compiler that links to OpenCV libraries. See Portable C Code Generation for Functions That Use OpenCV Library.
Introduced in R2015b