Exploring Visual Tracking Technology for Diverse Applications

1. Visual Tracking CMPUT 613/498

2. Why Visual Tracking? Related technology is cheap! Computers are fast enough! Applications abound Motion Control HCI Measurement

3. Applications:Watching a moving target • Camera + computer can determine how things move in an scene over time. Uses: • Security: e.g. monitoring people moving in a subway station or store • Measurement: Speed, alert on colliding trajectories etc.

4. ApplicationsHuman-Computer Interfaces • Camera + computer tracks motions of human user and interprets this in an on-line interaction. • Can interact with menus, buttons and e.g. drawing programs using hand movements as mouse movements, and gestures as clicking • Furthermore, can interpret physical interactions

5. ApplicationsHuman-Machine Interfaces • Camera + computer tracks motions of human user, interprets this and machine/robot carries out task. • Remote manipulation • Service robotics for the handicapped and elderly

6. ApplicationsHuman-Human Interfaces • Camera + computer tracks motions and expressions of human user, interprets, codes and transmits to render at remote location • Ultra low bandwidth video communication • Handheld video cell phone

7. Image streams -> Computer Digital Signal Image Processor Host Computer Camera Digitizer DISPLAY Analog Signal

8. A Modern Digital Camera (Firewire) IEEE 1394 Host Computer Camera DISPLAY X window Digital Signal

9. HW BANDWIDTH REQUIREMENTS Binary 1 bit * 640x480 * 30 = 9.2 Mbits/second Grey 1 byte * 640x480 * 30 = 9.2 Mbytes/second Color 3 bytes * 640x480 * 30 = 27.6 Mbytes/second (actually about 37 mbytes/sec) Typical operation: 8x8 convolution on grey image 64 multiplies + adds  600 Mflops Today’s PC’s are just getting to the point they can process images at frame rate

10. Characteristics of Video Processing abs( Image 1 - Image 2) = ? Note: Almost all pixels change! Constancy: The physical scene is the same How do we make use of this?

11. Fundamental types of video processing “Visual motion detecton” • Relating two adjacent frames: (small differences):

12. … Fundamental types of video processing “Visual Tracking” / Stabilization • Globally relating all frames: (large differences):

13. Types of tracking • Point tracking Extract the point (pixel location) of a particular image feature in each image. • Segmentation based tracking Define an identifying region property (e.g. color, texture statistics). Repeatedly extract this region in each image. • Stabilization based tracking Formulate image geometry and appearance equations and use these acquire image transform parameters for each image.

14. Point tracking • Simples technique. Already commercialized in motion capture systems. • Features commonly LED’s (visible or IR), special markers (reflective, patterns) • Detection: e.g. pick brightest pixel(s), cross correlate image to find best match for known pattern.

15. Region Tracking(Segmentation-Based) • Select a “cue:” • foreground enhancement • background subtraction • Segment • threshold • “clean up” • Compute region geometry • centroid (first moment) • orientation (second moment) • scale (second moment)

16. Regions • (color space): intrinsic coloration • Homogeneous region: is roughly constant • Textured region: has significant intensity gradients horizontally and vertically • Contour: (local) rapid change in contrast

17. Homogeneous Color Region: Photometry

18. Color Representation • is irradiance of region R • Color representation • DRM [Klinker et al., 1990]: if P is Lambertian, has matte line and highlight line • User selects matte pixels in R • PCA fits ellipsoid to matte cluster • Color similarity is defined by Mahalanobis distance

19. Homogeneous Region: Photometry PCA-fitted ellipsoid Sample

20. Color Extension (Contd.) • Hi = number of pixels in class i • Histograms are vectors of bin counts • Given histograms H and G, compare by • dense, stable signature • relies on segmentation • relative color and feature locations lost • affine transformations preserve area ratios of planar objects Color Histograms (Swain et al.)

21. Variability model: It = g(I0, pt) Incremental Estimation: From I0, It+1 and pt compute Dpt+1 Principles of Stabilization Tracking pt I0 It || I0 - g(It+1, pt+1) ||2 ==> min Visual Tracking = Visual Stabilization

22. Prediction Prior states predict new appearance Image warping Generate a “normalized view” Model inverse Compute error from nominal State integration Apply correction to state Reference Image Warping - p Dp Tracking Cycle Model Inverse

23. Stabilization tracking: Planar Case Planar Object + Linear camera => Affine motion model: u’i = A ui + d Subtract these: - - = nonsense Warping But these: - - = small variation It = g(pt, I0)

24. State information • Position • Orientation • Scale • Aspect ratio/Shear • Pose (S0(3); subgroup of Affine x R(2)) • Kinematic configuration (chains in SO(2) or SO(3)) • Non-rigid information (eigenmodes) • Photometric information (parametric illumination models) SO(2) + scale Affine (SL(2) x R(2))

25. Mathematical Formulation • Define a “warped image” • f(p,x) = x’ (warping function) • I(x,t) (image a location x at time t) • g(p,It) = (I(f(p,x1),t), I(f(p,x2),t), … I(f(p,xN),t))’ • Define the Jacobian of warping function • M(p,t) = • Consider “Incremental Least Squares” formulation • O(Dp, t+Dt) = || g(pt,It+Dt) – g(0,I0) ||2

26. Stabilization Formulation • Model • I0 = g(pt, It ) (image I, variation model g, parameters p) • DI = M(pt, It) Dp (local linearization M) • Define an error • et+1 = g(pt, It+1) - I0 • Close the loop • pt+1 = pt - (MT M)-1MT et+1 where M = M(pt,It) M is N x m and is time varying!

27. A Factoring Result (Hager & Belhumeur 1998) Suppose I = g(It, p) at pixel location u is defined as I(u) = I(f(p,u),t) and = L(u)S(p) Then M(p,It) = M0 S(p) where M0 = M(0,I0)

28. O(mN) operations G is m x N, e is N x 1 S is m x m Stabilization Revisited • In general, solve • [STG S] Dp = M0T et+1 where G = M0TM0 constant! • pt+1 = pt +Dp • If S is invertible, then • pt+1 = pt - S-T G et+1 where G = (M0TM0)-1M0T

29. On The Structure of M u’i = A ui + d Planar Object -> Affine motion model: X Y Rotation Scale Aspect Shear

30. Putting all together

31. Video

32. Motion Illumination Occlusion Tracking 3D Objects What is the set of all images of a 3D object?

33. 3D Case: Local Geometry Non-Planar Object: ui = A ui + bzi+ d x y rot z scale aspect rot x rot y

34. Observations: Lambertian object, single source, no cast shadows => 3D image space With shadows => a cone Empirical evidence suggests 5 to 6 basis images suffices 3D Case: Illumination Modeling Illumination basis: It = Ba + I0

35. Putting It Together • Variability model • I0 = g(pt, It ) + Ba (variation model g, parameters p, basis B) • DI = M(p) Dp + Ba (local linearization M) • Combine motion and illumination • et+1 = g(pt, It+1 ) - I0 - Bat • pt+1 = pt + (JT J)-1JT et+1 where J = [ M, B ] • Or, project into illumination kernel • pt+1 = pt + (MTN M)-1MT N et+1 where N = 1- B (BTB)-1BT

36. Handling Occlusion • Robust error metric • Dp = arg min r(e) • Huber & Dutter 1981 --- modified IRLS estimation • Dpk = (MTM)-1MT W(Dpk) e • Temporal propagation of weighting • Wt+1 = F (Wt)

37. Image Warping - Handling Occlusion Reference Weighting p Dp S Model Inverse

38. Pose

39. Related Work: Modalities • Color • Histogram [Birchfield, 1998; Bradski, 1998] • Volume [Wren et al., 1995; Bregler, 1997; Darrell, 1998] • Shape • Deformable curve [Kass et al.1988] • Template [Blake et al., 1993; Birchfield, 1998] • Example-based [Cootes et al., 1993; Baumberg & Hogg, 1994] • Appearance • Correlation [Lucas & Kanade, 1981; Shi & Tomasi, 1994] • Photometric variation [Hager & Belhumeur, 1998] • Outliers [Black et al., 1998; Hager & Belhumeur, 1998] • Nonrigidity [Black et al., 1998; Sclaroff & Isidoro, 1998]

40. Edges: An Illustrative Example • Classical approach to feature-based vision • Feature extraction (e.g. Canny edge detector) • Feature grouping (edges edgels) • Feature characterization (length, orientation, magnitude) • Feature matching (combinatorial problem!) • XVision approach • Develop a “canonical” edge with fixed state • Vertical step edge with position, orientation, strength • Assume prior information and use warping to acquire candidate region • Optimize detection algorithm to compute from/to state

41. Edges (XVision Approach) Rotational warp Apply a derivative of a triangle (IR filter) across rows Sum and interpolate to get position and orientation

42. Snakes • Contour C: continuous curve on smooth surface in • Snake S: projection of C to image • Curve types • Edge between regions on surface with contrasting properties • Line that contrasts with surface properties on both side • Silhouette of surface against contrasting background • General Algorithm: • Perform edge detection • Fit parametric or non-parametric curve to data

43. Snakes: Basic Approach • Parameterize a closed contour • or • Given a predicted state q, search radially for edges • Solve a least squares problem for new state

44. Snake: Details • Constrained deformations of B-spline templates [Blake et al., 1993] • zi: nearest edge at point r(si) along the curve with normal n(si) • X’ and S’ existing state and weight (covariance) estimate • Z0= 0, S0 = 0 • Iterate i = 1..N • vi = (zi – r(si)) . n(si) • h(si)t = n(si)t U(si) W • Si = Si-1 + 1/qi2 h(si)h(si)t • Zi = Zi-1 + 1/qi2 h(si)vi • Compute • X = X’ + (S’ + SN)-1ZN optional shape template where Q = WX + Q0

45. Snake: Alternative Approach • Perform gradient ascent on

46. Textured Region • Mean intensity difference between I and affine warp of template image [Shi & Tomasi, 1994]

47. Illumination

48. Pose and Illumination

49. XVision: Desktop Feature Tracking • Graphics-like system • Primitive features • Geometric constraints • Fast local image processing • Perturbation-based algorithms • Easily reconfigurable • Set of C++ classes • State-based conceptual model of information propagation • Goal • Flexible, fast, easy-to-use substrate

50. Prediction prior states predict new appearance Image rectification generate a “normalized view” Offset computation compute error from nominal State update apply correction to fundamental state Abstraction: Feature Tracking Cycle Offset Computation Window Manager s do ds State Update Prediction