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Scene-Consistent Detection of Feature Points in Video Sequences
Scene-Consistent Detection of Feature Points in Video Sequences
Operator for Extracting Certain Gradient Orientations
Operator for Extracting Certain Gradient Orientations
Operator for Extracting Certain Gradient Orientations
Operator for Extracting Certain Gradient Orientations
Operator for Extracting Certain Gradient Orientations
Operator for Extracting Certain Gradient Orientations
Tracking Algorithm
Tracking Algorithm
toys
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parking
parking
parking
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parking
parking
parking
parking
parking
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traffic
traffic
traffic
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traffic
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Measures for Evaluation of Scene-Consistency (cont
Measures for Evaluation of Scene-Consistency (cont
Completeness:
Completeness:
Completeness:
Completeness:
Completeness:
Completeness:
Completeness:
Completeness:
Completeness:
Completeness:
Completeness:
Completeness:
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Scene-Consistent Detection of Feature Points in Video Sequences

содержание презентации «Scene-Consistent Detection of Feature Points in Video Sequences.ppt»
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1Scene-Consistent Detection of Feature 11parking. #200. #50. #100. #225. #150.
Points in Video Sequences. Ariel Tankus #250.
& Yehezkel Yeshurun. CVPR - Dec. 2001. 12traffic. #5. #55. #65. #10. #70. #15.
Tel-Aviv. University. 13Evaluating the Performance of the
2Outline: Relating convexity-based Algorithm. Two measures for evaluating
detection of feature points to scene performance of scene-consistent point
geometry. Feature points tracking tracking algorithms. Each measure aimed at
algorithm. Comparison with two other a different task: Maximal tracking time.
methods. Measures for evaluation of Correspondence of points in successive
tracking algorithms w.r.t 3D frames. Their common goal: to quantify the
scene-consistency. consistency of tracks with 3D scene.
3Task Definition: Robust detection of 14Measures for Evaluation of
scene-consistent features in video Scene-Consistency. Completeness: A track
sequences. Object recognition. is complete if the same 3D scene point is
Correspondence points for recovering 3D being tracked, up to a certain level of
characteristics of the scene. Convexity. noise, in every frame where it appears.
Goals: Intrinsic Property: Correct 3D point of track T = 3D point
4Operator for Feature Detection. ? tracked for the longest time under track
Detect convex or concave image domains. T.
Detect local “circles” where the gradient 15Measures for Evaluation of
of the intensity function points outward Scene-Consistency (cont.). Stability:
along the whole circle. The gradient 16Tracking Comparison. We compare the
points in all orientations along the Darg-based algorithm with two other
“circles”. (motivation). Equivalently: algorithms: Junction detection
5Operator for Extracting Certain (Lindeberg). with automatic scale
Gradient Orientations. At the selection. Tracking by Kalman filter. KLT
discontinuity ray of the arctan: Yarg??. (Kanade-Lucas-Tomasi). Tracker based on
Darg - An isotropic variant of Yarg. affine image change model. Features
6Response of Yarg to the Intensity maximize tracking quality.
Surface. Examine Yarg in well behaving 17Completeness: Stability:
image domains. Intensity is twice 18Experimental Results. Darg is more
continuously differentiable. We examine stable the Junction detection, and
all possible intensity configurations. sometimes more than KLT. Sometimes Darg
Four of them lead to infinite Yarg equates with KLT. Darg completeness is at
response. The basic observation: least comparable to that of Junction
7Response of Yarg to the Intensity detection or KLT, and sometimes even
Surface (cont.). ? The cases include: Some better. Darg has significantly lower
configurations where is a local extrema of no-tracking time (Darg: 4, KLT: 81, J.D.:
, and some configurations where one side 121 frames).
of is flat, but the other is convex or 19Summary. Convexity-based method for
concave. Only specific differential scene-consistent feature points detection
geometry structures of the intensity in video sequences. Detection relates to
function causes Yarg??. specific features of the intensity
8Response to Local 3D Scene Structure. surface. These intensity features relate
? ? Yarg responds to certain geometric to geometric features of the 3D object.
features of the 3D scene object. Yarg?? 20Summary (cont.). A stable point
for certain elliptic, hyperbolic or tracking algorithm is described (2D Kalman
parabolic points on a Lambertian 3D filter). Two measures serve in a
surface illuminated by a point light comparison with two other tracking
source at infinity. methods. Completeness: Maximizes tracking
9Tracking Algorithm. Stable points: time of a 3D scene point. Stability:
points where . These points are the only Consistent tracking of 3D points between
input to the point tracker. successive frames.
10toys. #28. #8. #16. #36. #24. #44.
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Scene-Consistent Detection of Feature Points in Video Sequences

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