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Vanishing Point Detection and Tracking Jeongkyun Lee.

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Presentation on theme: "Vanishing Point Detection and Tracking Jeongkyun Lee."— Presentation transcript:

1 Vanishing Point Detection and Tracking Jeongkyun Lee

2 Vanishing Point Related Works –Vanishing Point Detection –Vanishing Point Tracking Proposed Method Result Contents 2

3 A set of parallel lines in the scene is projected onto a set of lines in the image that meet in a common point, with a pin-hole camera. Vanishing Point 3

4 Intrinsic camera calibration Plane rectification 3D reconstruction Orientation estimation Stabilization Etc. Vanishing Point 4

5 Gaussian unit sphere A great circle: A projection of a line onto the unit sphere Vanishing direction Normal vectors of great circles Vanishing Point 5

6 Rotational dependence the vanishing points, are not affected by the camera translation, but are affected only by the camera rotation. Vanishing Point 6 Homogeneous coordinates Euclidean transformation Rotation + Translation Vanishing point A projection of a vanishing direction

7 Work space –Image space, Gaussian sphere, Projective space, etc. –Hough transform, Thales theory, etc. –Bounded area(a tessellated space or accumulation cells), Unbounded area Clustering technique –Accumulation(Voting), RANSAC-based, etc. –Three orthogonal direction(Manhattan World), Coplanar, etc. Estimation technique –EM algorithm, SVD, Weighted least squre Vanishing Point Detection 7

8 Nieto at al. PRL 2011 – line vanishing point EM algorithm Almansa at al. TPAMI 2003 –Image plane prior vanishing point Vanishing Point Detection 8

9 1. VP, VP 2. VPs Orthogonal tripod Vanishing Point Tracking 9

10 Hornacek at al. CVPR 2011 –RANSAC-based / Manhattan world / SVD / tripod matching Vanishing Point Tracking 10

11 VP Line detection VP estimation Work space error image plane VP Rotation Tessellated weight Related Works 11

12 1. System modeling State vector Dynamic model Measurement model Real-time Orientation and Vanishing Point Tracking 12

13 2. Initialization Vanishing Direction: VP detection open source 3. Measurement acquisition: Line tracking EDLines Line segment (~10ms) Line, gradient 4. Feature management New line feature: line vanishing direction threshold Line removal: vanishing direction line threshold, line Real-time Orientation and Vanishing Point Tracking 13

14 Synthetic data Result 14

15 Singularity 1 Result 15

16 Singularity 2 Result 16

17 ROVE + Line tracking + Line feature management Result 17

18 ROVE + EDLines Result 18

19 Computational time Matlab Feature management : 3~5 ms Prediction : 2 ms Measurement search ( Line detection + Line matching ) : 40~45 ms Update : 1ms Result 19

20 20 Thank you!

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