特征跟踪和光流是用于恢复操作,特征跟踪是指提取视觉特征(角落,纹理区域),并在多个帧上“跟踪”它们,光流(Optical Flow)是指从时空图像亮度变化中恢复每个像素处的图像运动。两个问题均可以使用registration方法解决。

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1.Feature Tracking and Optical Flow Computer Vision Jia-Bin Huang, Virginia Tech Many slides from D . Hoiem

2.Administrative Stuffs HW 1 due 11:59 PM Sept 19 Submission through Canvas Regular office hour by Jia-Bin 3:00- 4:00 PM, Friday Sept 16 Bonus office hour by Akrit 10:30 AM – 11: 30 AM, Monday Sept 19 HW 1 Competition: Edge Detection Submission link Leaderboard

3.Administrative Stuffs HW 1 due 11:59 PM Sept 19 Submission through Canvas Regular office hour by Jia-Bin 3:00- 4:00 PM, Friday Sept 16 Bonus office hour by Akrit 10:30 AM – 11: 30 AM, Monday Sept 19 HW 1 Competition: Edge Detection Submission link Leaderboard

4.This class: recovering motion Feature tracking Extract visual features (corners, textured areas) and “track” them over multiple frames Optical flow Recover image motion at each pixel from spatio -temporal image brightness variations B. Lucas and T. Kanade . An iterative image registration technique with an application to stereo vision. In Proceedings of the International Joint Conference on Artificial Intelligence , 1981. Two problems, one registration method

5.Feature tracking Many problems, such as structure from motion require matching points If motion is small, tracking is an easy way to get them

6.Feature tracking - Challenges Figure out which features can be tracked Efficiently track across frames Some points may change appearance over time (e.g., due to rotation, moving into shadows, etc.) Drift: small errors can accumulate as appearance model is updated Points may appear or disappear: need to be able to add/delete tracked points

7.Feature tracking Given two subsequent frames, estimate the point translation Key assumptions of Lucas- Kanade Tracker Brightness constancy: projection of the same point looks the same in every frame Small motion: points do not move very far Spatial coherence: points move like their neighbors I ( x , y , t ) I ( x , y , t+1 )

8.Brightness Constancy Equation: Take Taylor expansion of I(x+u, y+v, t+1) at (x,y,t) to linearize the right side: The brightness constancy constraint I ( x , y , t ) I ( x , y , t+1 ) So: Image derivative along x Difference over frames

9.How many equations and unknowns per pixel? The component of the motion perpendicular to the gradient (i.e., parallel to the edge) cannot be measured edge ( u , v ) ( u ’, v ’) gradient ( u + u ’, v + v ’) If ( u , v ) satisfies the equation, so does ( u+u’ , v+v’ ) if One equation (this is a scalar equation!), two unknowns (u,v) Can we use this equation to recover image motion (u,v) at each pixel? The brightness constancy constraint

10.The aperture problem Actual motion

11.The aperture problem Perceived motion

12.The barber pole illusion http://en.wikipedia.org/wiki/Barberpole_illusion

13.The barber pole illusion http://en.wikipedia.org/wiki/Barberpole_illusion

14.Solving the ambiguity… How to get more equations for a pixel? Spatial coherence constraint Assume the pixel’s neighbors have the same ( u,v ) If we use a 5x5 window, that gives us 25 equations per pixel B. Lucas and T. Kanade . An iterative image registration technique with an application to stereo vision. In Proceedings of the International Joint Conference on Artificial Intelligence , pp. 674–679, 1981.

15.Least squares problem: Solving the ambiguity…

16.Matching patches across images Overconstrained linear system The summations are over all pixels in the K x K window Least squares solution for d given by

17.Conditions for solvability Optimal (u, v) satisfies Lucas-Kanade equation Does this remind you of anything? When is this solvable? I.e., what are good points to track? A T A should be invertible A T A should not be too small due to noise eigenvalues  1 and  2 of A T A should not be too small A T A should be well-conditioned  1 /  2 should not be too large (  1 = larger eigenvalue) Criteria for Harris corner detector

18.Eigenvectors and eigenvalues of A T A relate to edge direction and magnitude The eigenvector associated with the larger eigenvalue points in the direction of fastest intensity change The other eigenvector is orthogonal to it M = A T A is the second moment matrix ! (Harris corner detector…)

19.Low-texture region gradients have small magnitude small l 1 , small l 2

20.Edge gradients very large or very small large l 1 , small l 2

21.High-texture region gradients are different, large magnitudes large l 1 , large l 2

22.The aperture problem resolved Actual motion

23.The aperture problem resolved Perceived motion

24.Dealing with larger movements: Iterative refinement Initialize ( x’,y ’) = ( x,y ) Compute ( u,v ) by Shift window by (u, v): x’ = x’+u ; y’= y’+v ; Recalculate I t Repeat steps 2-4 until small change Use interpolation for subpixel values 2 nd moment matrix for feature patch in first image displacement I t = I(x’, y’, t+1) - I(x, y, t) Original (x,y) position

25.image I image J Gaussian pyramid of image 1 (t) Gaussian pyramid of image 2 (t+1) image 2 image 1 Dealing with larger movements: coarse-to-fine registration run iterative L-K run iterative L-K upsample . . .

26.Shi- Tomasi feature tracker Find good features using eigenvalues of second-moment matrix (e.g., Harris detector or threshold on the smallest eigenvalue ) Key idea: “good” features to track are the ones whose motion can be estimated reliably Track from frame to frame with Lucas-Kanade This amounts to assuming a translation model for frame-to-frame feature movement Check consistency of tracks by affine registration to the first observed instance of the feature Affine model is more accurate for larger displacements Comparing to the first frame helps to minimize drift J. Shi and C. Tomasi. Good Features to Track . CVPR 1994.

27.Tracking example J. Shi and C. Tomasi. Good Features to Track . CVPR 1994.

28.Summary of KLT tracking Find a good point to track ( harris corner) Use intensity second moment matrix and difference across frames to find displacement Iterate and use coarse-to-fine search to deal with larger movements When creating long tracks, check appearance of registered patch against appearance of initial patch to find points that have drifted

29.Implementation issues Window size Small window more sensitive to noise and may miss larger motions (without pyramid) Large window more likely to cross an occlusion boundary (and it’s slower) 15x15 to 31x31 seems typical Weighting the window Common to apply weights so that center matters more (e.g., with Gaussian)

30.Why not just do local template matching? Slow (need to check more locations) Does not give subpixel alignment (or becomes much slower) Even pixel alignment may not be good enough to prevent drift May be useful as a step in tracking if there are large movements

31.Picture courtesy of Selim Temizer - Learning and Intelligent Systems (LIS) Group, MIT Optical flow Vector field function of the spatio-temporal image brightness variations

32.Motion and perceptual organization Sometimes, motion is the only cue

33.Motion and perceptual organization Even “impoverished” motion data can evoke a strong percept G. Johansson, “Visual Perception of Biological Motion and a Model For Its Analysis", Perception and Psychophysics 14, 201-211, 1973.

34.Motion and perceptual organization Even “impoverished” motion data can evoke a strong percept G. Johansson, “Visual Perception of Biological Motion and a Model For Its Analysis", Perception and Psychophysics 14, 201-211, 1973.

35.Uses of motion Estimating 3D structure Segmenting objects based on motion cues Learning and tracking dynamical models Recognizing events and activities Improving video quality (motion stabilization)

36.Motion field The motion field is the projection of the 3D scene motion into the image What would the motion field of a non-rotating ball moving towards the camera look like?

37.Optical flow Definition: optical flow is the apparent motion of brightness patterns in the image Ideally, optical flow would be the same as the motion field Have to be careful: apparent motion can be caused by lighting changes without any actual motion Think of a uniform rotating sphere under fixed lighting vs. a stationary sphere under moving illumination

38.Lucas-Kanade Optical Flow Same as Lucas-Kanade feature tracking, but for each pixel As we saw, works better for textured pixels Operations can be done one frame at a time, rather than pixel by pixel Efficient

39.Multi-resolution Lucas Kanade Algorithm

40.40 Iterative Refinement Iterative Lukas-Kanade Algorithm Estimate displacement at each pixel by solving Lucas-Kanade equations Warp I(t) towards I(t+1) using the estimated flow field - Basically, just interpolation Repeat until convergence * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003

41.image I image J Gaussian pyramid of image 1 (t) Gaussian pyramid of image 2 (t+1) image 2 image 1 Coarse-to-fine optical flow estimation run iterative L-K run iterative L-K warp & upsample . . .

42.image I image H Gaussian pyramid of image 1 Gaussian pyramid of image 2 image 2 image 1 u=10 pixels u=5 pixels u=2.5 pixels u=1.25 pixels Coarse-to-fine optical flow estimation

43.Example * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003

44.Multi-resolution registration * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003

45.Optical Flow Results * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003

46.Optical Flow Results * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003

47.Errors in Lucas-Kanade The motion is large Possible Fix: Keypoint matching A point does not move like its neighbors Possible Fix: Region-based matching Brightness constancy does not hold Possible Fix: Gradient constancy

48.State-of-the-art optical flow Start with something similar to Lucas- Kanade + gradient constancy + energy minimization with smoothing term + region matching + keypoint matching (long-range) Large displacement optical flow , Brox et al., CVPR 2009 Region-based +Pixel-based +Keypoint-based

49.Things to remember Major contributions from Lucas, Tomasi , Kanade Tracking feature points Optical flow Stereo (later) Structure from motion (later) Key ideas By assuming brightness constancy, truncated Taylor expansion leads to simple and fast patch matching across frames Coarse-to-fine registration

50.Next week HW 1 due Monday Object/image alignment