homework-2

Motion Estimation

Optical Flow — Horn & Schunck Algorithm

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Usage

HornSchunck() class is written in Python. It returns optical flow velocities (u,v) for given parameters:

• alpha : (float) the regularization coefficient of smoothness constraint
• maxIter : (int) maximum number of iterations required to obtain flow velocities
• applyGauss : (bool) apply Gaussian smoothing as a preprocess {False by default}
• GaussKernel : (tuple) Gaussian Filter kernel size {(15,15) by default}

Initialize an object of HornSchunck() class with an arbitrary name, optFlow() is suggested.

optFlow = HornSchunck(alpha=alpha,maxIter=maxIter)
optFlow.applyGauss  = True 
optFlow.GaussKernel = (5,5)

Then use estimate() method to calculate optical flow velocities (u,v). After execution, getFlowField() method returns the colorized flow field.

(u,v) = optFlow.estimate(frame1,frame2) 
flowField = optFlow.getFlowField() 

Prediction of target frame from anchor via flow velocities is possible with predict() method. Moreover, getCollage() method puts 4 frames together to show a collage.

anchorP = optFlow.predict() 
collage = optFlow.getCollage(f1_idx,f2_idx)

Reference Paper

[1] Horn and Schunck. (1981). Determining Optical Flow

Credits

Utilized from OpticalFlow_Visualization by Tom Runia (tomrunia@github) for optical flow field colorization.