Scene-Consistent Detection of Feature Points in Video Sequences - PowerPoint PPT Presentation

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Scene-Consistent Detection of Feature Points in Video Sequences

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  1. CVPR - Dec. 2001 Scene-Consistent Detection ofFeature Points in Video Sequences Ariel Tankus & Yehezkel Yeshurun Tel-Aviv University

  2. Outline: • Relating convexity-based detection of feature points to scene geometry. • Feature points tracking algorithm. • Comparison with two other methods. • Measures for evaluation of tracking algorithms w.r.t 3D scene-consistency.

  3. Task Definition: Robust detection of scene-consistent features in video sequences. Goals: • Object recognition. • Correspondence points for recovering 3D characteristics of the scene. Intrinsic Property: Convexity.

  4. Operator for Feature Detection (motivation) Detect convex or concave image domains. Detect local “circles” where the gradient of the intensity function points outward along the whole circle. Equivalently:  The gradient points in all orientations along the “circles”.

  5. Gradient Argument Yarg derive Operator for Extracting Certain Gradient Orientations At the discontinuity ray of the arctan: Yarg. Darg - An isotropic variant of Yarg.

  6. If at , then has a jump discontinuity there. Response of Yarg to theIntensity Surface • Examine Yarg in well behaving image domains. • Intensity is twice continuously differentiable. The basic observation: • We examine all possible intensity configurations. • Four of them lead to infinite Yarg response.

  7. Response of Yarg to theIntensity Surface (cont.) The cases include: Some configurations where is a local extrema of , and some configurations where one side of is flat, but the other is convex or concave.  Only specific differential geometry structures of the intensity function causes Yarg.

  8. For certain intensity function configurations, if has a jump discontinuity, then z(x,y) is: elliptic, hyperbolic or parabolic there. Response to Local 3D Scene Structure  • Yarg for certain elliptic, hyperbolic or parabolic points on a Lambertian 3D surface illuminated by a point light source at infinity.  • Yarg responds to certain geometric features of the 3D scene object.

  9. Tracking Algorithm • Stable points: points where . • These points are the only input to the point tracker.

  10. #28 #8 #16 #36 toys #24 #44

  11. #200 #50 parking #100 #225 #150 #250

  12. #5 #55 traffic #65 #10 #70 #15

  13. Evaluating the Performance of the Algorithm • Two measures for evaluating performance of scene-consistent point tracking algorithms. • Each measure aimed at a different task: • Maximal tracking time. • Correspondence of points in successive frames. • Their common goal: to quantify the consistency of tracks with 3D scene.

  14. Time(correct 3D point is tracked) Completeness of track T = Time(correct 3D point appears in video) Measures for Evaluation of Scene-Consistency • Completeness: • A track is complete if the same 3D scene point is being tracked, up to a certain level of noise, in every frame where it appears. Correct 3D point of track T = 3D point tracked for the longest time under track T

  15. Stability of tracking in frames fi, fi+1 = #tracks following the correct 3D point in both fi, fi+1 #tracks containing points in both fi, fi+1 Measures for Evaluation of Scene-Consistency (cont.) • Stability:

  16. Tracking Comparison • We compare the Darg-based algorithm with two other algorithms: • Junction detection (Lindeberg).with automatic scale selection.Tracking by Kalman filter. • KLT (Kanade-Lucas-Tomasi). Tracker based on affine image change model. Features maximize tracking quality.

  17. Completeness: Stability:

  18. Experimental Results • Darg is more stable the Junction detection, and sometimes more than KLT. Sometimes Darg equates with KLT. • Dargcompleteness is at least comparable to that of Junction detection or KLT, and sometimes even better. • Darg has significantly lower no-trackingtime (Darg: 4, KLT: 81, J.D.: 121 frames).

  19. Summary • Convexity-based method for scene-consistent feature points detection in video sequences. • Detection relates to specific features of the intensity surface. • These intensity features relate to geometric features of the 3D object.

  20. Summary (cont.) • A stable point tracking algorithm is described (2D Kalman filter). • Two measures serve in a comparison with two other tracking methods. • Completeness: Maximizes tracking time of a 3D scene point. • Stability: Consistent tracking of 3D points between successive frames.