Vanishing Point Detection and Tracking Jeongkyun Lee.
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Transcript of Vanishing Point Detection and Tracking Jeongkyun Lee.
![Page 1: Vanishing Point Detection and Tracking Jeongkyun Lee.](https://reader034.fdocuments.us/reader034/viewer/2022051210/55179f9b5503460e6e8b5d4b/html5/thumbnails/1.jpg)
Vanishing Point Detection and Track-ing
Jeongkyun Lee
![Page 2: Vanishing Point Detection and Tracking Jeongkyun Lee.](https://reader034.fdocuments.us/reader034/viewer/2022051210/55179f9b5503460e6e8b5d4b/html5/thumbnails/2.jpg)
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Vanishing Point
Related Works– Vanishing Point Detection– Vanishing Point Tracking
Proposed Method
Result
Contents
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A set of parallel lines in the scene is pro-jected onto a set of lines in the image that meet in a common point, with a pin-hole cam-era.
Vanishing Point
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Intrinsic camera calibration Plane rectification 3D reconstruction Orientation estimation Stabilization Etc.
Vanishing Point
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Gaussian unit sphere A great circle: A projection of a line onto the unit
sphere Vanishing direction ⊥ Normal vectors of great circles
Vanishing Point
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Rotational dependence the vanishing points, are not affected by the cam-
era translation, but are affected only by the camera ro-tation.
Vanishing Point
Homogeneous coordinates
Euclidean transformationRotation + Translation
Vanishing pointA projection of a vanishing direction
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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
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Nieto at al. PRL 2011– 추출된 line 들과 vanishing point 를 동시에 EM algorithm
을 통해 추정
Almansa at al. TPAMI 2003– Image plane 위에서 어떠한 prior 도 없이 vanishing point
를 추정하는 방법
Vanishing Point Detection
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1. 매 프레임 VP 검출 후 , 가까운 VP 를 매칭하는 방법
2. 매 프레임 검출된 VPs 로부터 Orthogonal 한 tri-pod 를 형성하여 매칭하는 방법
Vanishing Point Tracking
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Hornacek at al. CVPR 2011– RANSAC-based / Manhattan world / SVD / tripod match-
ing
Vanishing Point Tracking
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VP 와 관련된 문제들
① Line detection 의 오차가 VP estimation 의 정확도에 큰 영향을 줌에 따라 이를 최소화 하는 방법 연구
② Work space 의 변환 시 발생하는 error 를 최소화 하기 위해 im-age plane 에서 연산
③ VP 를 이용하여 Rotation 추정
④ Tessellated 영역에 대해 동일한 weight 를 가질 수 있도록 하기 위한 영역 설정 방법
Related Works
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1. System modeling State vector
Dynamic model
Measurement model
Real-time Orientation and Vanishing Point Tracking
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2. Initialization Vanishing Direction: VP detection open source 사용
3. Measurement acquisition: Line tracking EDLines 를 이용하여 Line segment 검출 (~10ms) Line 의 길이 , gradient 의 크기 및 방향 등을 비교하여 매칭
4. Feature management New line feature: 검출된 line 중에 vanishing direction
과의 평행성이 일정 회전 동안 threshold 이하 일 때 Line removal: 추정된 vanishing direction 과 검출된 line
의 평행성이 일정 threshold 이하 일 때 , 또는 line 이 일정 프레임 이상 검출되지 않을 때
Real-time Orientation and Vanishing Point Tracking
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Synthetic data
Result
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Singularity 1
Result
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Singularity 2
Result
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ROVE + Line tracking + Line feature man-agement
Result
![Page 18: Vanishing Point Detection and Tracking Jeongkyun Lee.](https://reader034.fdocuments.us/reader034/viewer/2022051210/55179f9b5503460e6e8b5d4b/html5/thumbnails/18.jpg)
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ROVE + EDLines
Result
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Computational time
Matlab 에서
Feature management : 3~5 ms Prediction : 2 ms Measurement search ( Line detection + Line matching ) :
40~45 ms Update : 1ms
Result
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Thank you!