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Optical Flow Methods CISC 489/689 Spring 2009 University of Delaware

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Outline Review of Optical Flow Constraint, Lucas- Kanade, Horn and Schunck Methods Lucas-Kanade Meets Horn and Schunck 3D Regularization Techniques for solving optical flow Confidence Measures in Optical Flow

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Optical Flow Constraint

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Interpretation Constraint Line

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Lucas-Kanade Method * =

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Local Method, window based Cannot solve for optical flow everywhere Robust to noise Figures from Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods ANDR´ES BRUHN AND JOACHIM WEICKERT, 2005

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Dense optical Flow Lacks Smoothness Figures from Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods ANDR´ES BRUHN AND JOACHIM WEICKERT, 2005

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Horn and Schunck Method Euler-Lagrange Equations

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Horn and Schunck Method Figures from Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods ANDR´ES BRUHN AND JOACHIM WEICKERT, 2005 Global Method Estimates flow everywhere Sensitive to noise Oversmooths the edges

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Why combine them? Need dense flow estimate Robust to noise Preserve discontinuities

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Combining the two…

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Combined Local Global Method Euler-Lagrange Equations Average Error Standard Deviation Lucas&Kanade 4.3 (density 35%) Horn&Schunk Combining local and global Table: Courtesy - Darya Frolova, Recent progress in optical flow

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Preserving discontinuities Gaussian Window does not preserve discontinuities Solutions – Use bilateral filtering – Add gradient constancy

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Bilateral support window Images: Courtesy, Darya Frolova, Recent progress in optical flow

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Robust statistics – simple example Find “best” representative for the set of numbers L2: L1: xixi Influence of x i on E : x i → x i + ∆ Outliers influence the most proportional to Majority decides equal for all x i Slide: Courtesy - Darya Frolova, Recent progress in optical flow

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Robust statistics many ordinary people a very rich man Oligarchy Votes proportional to the wealth Democracy One vote per person wealth like in L2 norm minimization like in L1 norm minimization Slide: Courtesy - Darya Frolova, Recent progress in optical flow

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Combination of two flow constraints robust: L1 robust in presence of outliers – non-smooth: hard to analyze usual: L2 easy to analyze and minimize – sensitive to outliers robust regularized smooth: easy to analyze robust in presence of outliers ε [A. Bruhn, J. Weickert, 2005] Towards ultimate motion estimation: Combining highest accuracy with real-time performance Slide: Courtesy - Darya Frolova, Recent progress in optical flow

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Robust statistics

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3D Regularization accounted for spatial regularization If velocities do not change suddenly with time, can we regularize in time as well?

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3D Regularization

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Multiresolution estimation 21 image I image J Gaussian pyramid of image 1Gaussian pyramid of image 2 Image 2 image 1 run iterative estimation warp & upsample......

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Multi-resolution Lucas Kanade Algorithm Compute Iterative LK at highest level For Each Level i Take flow u(i-1), v(i-1) from level i-1 Upsample the flow to create u*(i), v*(i) matrices of twice resolution for level i. Multiply u*(i), v*(i) by 2 Compute I t from a block displaced by u*(i), v*(i) Apply LK to get u’(i), v’(i) (the correction in flow) Add corrections u’(i), v’(i) to obtain the flow u(i), v(i) at the i th level, i.e., u(i)=u*(i)+u’(i), v(i)=v*(i)+v’(i)

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Comparison of errors For Yosemite sequence with clouds Table: Courtesy - Darya Frolova, Recent progress in optical flow

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Solving the system How to solve? 2 components of success: fast convergence good initial guess Start with some initial guess and apply some iterative method

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smoothing Relaxation schemes have smoothing property: Only neighboring pixels are coupled in relaxation scheme It may take thousands of iterations to propagate information to large distance Relaxation smoothes the error

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Relaxation smoothes the error Examples 2D case: 1D case: Error of initial guess Error after 5 relaxation Error after 15 relaxations

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Idea: coarser grid On a coarser grid low frequencies become higher Hence, relaxations can be more effective initial grid – fine grid coarse grid – we take every second point

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Multigrid 2-Level V-Cycle 1. Iterate ⇒ error becomes smooth 2. Transfer error equation to the coarse level ⇒ low frequencies become high 3. Solve for the error on the coarse level ⇒ good error estimation 4. Transfer error to the fine level 5. Correct the previous solution 6. Iterate ⇒ remove interpolation artifacts

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make iteration process faster (on the coarse grid we can effectively minimize the error) obtain better initial guess (solve directly on the coarsest grid) Coarse grid - advantages Coarsening allows: go to the coarsest grid solve here the equation to find interpolate to the finer grid

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Multigrid approach – Full scheme

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Confidence Metric Intrinsic in Local Methods How to evaluate for global methods? – Edge strength? Doesn’t work (Barron et al.,1994)

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Confidence Metric Histogram of error contribution Error Number of pixels

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Confidence Metric

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More Results

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Further Reading Combining the advantages of local and global optic flow methods (“Lucas/Kanade meets Horn/Schunck”) A. Bruhn, J. Weickert, C. Schnörr, High accuracy optical flow estimation based on a theory for warping T. Brox, A. Bruhn, N. Papenberg, J. Weickert, Real-Time Optic Flow Computation with Variational Methods A. Bruhn, J. Weickert, C. Feddern, T. Kohlberger, C. Schnörr, Towards ultimate motion estimation: Combining highest accuracy with real-time performance. A. Bruhn, J. Weickert, 2005 Bilateral filtering-based optical flow estimation with occlusion detection. J.Xiao, H.Cheng, H.Sawhney, C.Rao, M.Isnardi, 2006

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