# Problem definition: optical flow

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1 Motion Estimation Why estimate motion? Lots of uses Track object behavior Correct for camera jitter (stabilization) Align images (mosaics) 3D shape reconstruction Special effects Today s Readings Trucco & Verri, (skip 8.3.3, read only top half of p. 199) Numerical Recipes (Newton-Raphson), 9.4 (first four pages) Optical flow Problem definition: optical flow How to estimate pixel motion from image H to image I? Solve pixel correspondence problem given a pixel in H, look for nearby pixels of the same color in I Key assumptions color constancy: a point in H looks the same in I For grayscale images, this is brightness constancy small motion: points do not move very far This is called the optical flow problem

2 Optical flow constraints (grayscale images) Optical flow equation Combining these two equations Let s look at these constraints more closely brightness constancy: Q: what s the equation? In the limit as u and v go to zero, this becomes exact small motion: (u and v are less than 1 pixel) suppose we take the Taylor series expansion of I: Optical flow equation Aperture problem Q: how many unknowns and equations per pixel? Intuitively, what does this constraint mean? The component of the flow in the gradient direction is determined The component of the flow parallel to an edge is unknown This explains the Barber Pole illusion

3 Aperture problem Solving the aperture problem How to get more equations for a pixel? Basic idea: impose additional constraints most common is to assume that the flow field is smooth locally one method: pretend the pixel s neighbors have the same (u,v)» If we use a 5x5 window, that gives us 25 equations per pixel! RGB version How to get more equations for a pixel? Basic idea: impose additional constraints most common is to assume that the flow field is smooth locally one method: pretend the pixel s neighbors have the same (u,v)» If we use a 5x5 window, that gives us 25*3 equations per pixel! Lukas-Kanade flow Prob: we have more equations than unknowns Solution: solve least squares problem minimum least squares solution given by solution (in d) of: The summations are over all pixels in the K x K window This technique was first proposed by Lukas & Kanade (1981) described in Trucco & Verri reading

4 Conditions for solvability Eigenvectors of A T A Optimal (u, v) satisfies Lucas-Kanade equation When is This Solvable? A T A should be invertible A T A should not be too small due to noise eigenvalues λ 1 and λ 2 of A T A should not be too small A T A should be well-conditioned λ 1 / λ 2 should not be too large (λ 1 = larger eigenvalue) Suppose (x,y) is on an edge. What is A T A? gradients along edge all point the same direction gradients away from edge have small magnitude is an eigenvector with eigenvalue What s the other eigenvector of A T A? let N be perpendicular to derive on board N is the second eigenvector with eigenvalue 0 The eigenvectors of A T A relate to edge direction and magnitude Edge Low texture region large gradients, all the same large λ 1, small λ 2 gradients have small magnitude smallλ 1, small λ 2

5 High textured region Observation This is a two image problem BUT Can measure sensitivity by just looking at one of the images! This tells us which pixels are easy to track, which are hard very useful later on when we do feature tracking... gradients are different, large magnitudes large λ 1, large λ 2 Errors in Lukas-Kanade What are the potential causes of errors in this procedure? Suppose A T A is easily invertible Suppose there is not much noise in the image When our assumptions are violated Brightness constancy is not satisfied The motion is not small A point does not move like its neighbors window size is too large what is the ideal window size? Improving accuracy Recall our small motion assumption This is not exact To do better, we need to add higher order terms back in: This is a polynomial root finding problem Can solve using Newton s method 1D case Also known as Newton-Raphson method on board Today s reading (first four pages)» Approach so far does one iteration of Newton s method Better results are obtained via more iterations

6 Iterative Refinement Iterative Lukas-Kanade Algorithm 1. Estimate velocity at each pixel by solving Lucas-Kanade equations 2. Warp H towards I using the estimated flow field - use image warping techniques 3. Repeat until convergence Revisiting the small motion assumption Is this motion small enough? Probably not it s much larger than one pixel (2 nd order terms dominate) How might we solve this problem? Reduce the resolution! Coarse-to-fine optical flow estimation u=1.25 pixels u=2.5 pixels u=5 pixels image H u=10 pixels image I Gaussian pyramid of image H Gaussian pyramid of image I

7 Coarse-to-fine optical flow estimation Optical flow result run iterative L-K warp & upsample run iterative L-K... image JH Gaussian pyramid of image H image I Gaussian pyramid of image I David Dewey morph Motion tracking Suppose we have more than two images How to track a point through all of the images? In principle, we could estimate motion between each pair of consecutive frames Given point in first frame, follow arrows to trace out it s path Problem: DRIFT» small errors will tend to grow and grow over time the point will drift way off course Feature Tracking Choose only the points ( features ) that are easily tracked How to find these features? windows where has two large eigenvalues Called the Harris Corner Detector Feature Detection

8 Tracking features Feature tracking Compute optical flow for that feature for each consecutive H, I When will this go wrong? Occlusions feature may disappear need mechanism for deleting, adding new features Changes in shape, orientation allow the feature to deform Changes in color Large motions will pyramid techniques work for feature tracking? Handling large motions L-K requires small motion If the motion is much more than a pixel, use discrete search instead Given feature window W in H, find best matching window in I Minimize sum squared difference (SSD) of pixels in window Solve by doing a search over a specified range of (u,v) values this (u,v) range defines the search window Tracking Over Many Frames Feature tracking with m frames 1. Select features in first frame 2. Given feature in frame i, compute position in i+1 3. Select more features if needed 4. i = i If i < m, go to step 2 Issues Discrete search vs. Lucas Kanade? depends on expected magnitude of motion discrete search is more flexible Compare feature in frame i to i+1 or frame 1 to i+1? affects tendency to drift.. How big should search window be? too small: lost features. Too large: slow Incorporating Dynamics Idea Can get better performance if we know something about the way points move Most approaches assume constant velocity or constant acceleration Use above to predict position in next frame, initialize search

9 Feature tracking demo Image alignment Oxford video MPEG application of feature tracking Goal: estimate single (u,v) translation for entire image Easier subcase: solvable by pyramid-based Lukas-Kanade Application: Rotoscoping (demo)

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