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Practical Camera Auto-Calibration Based on Object Appearance and Motion for Traffic
Scene Visual Surveillance
Zhaoxiang Zhang, Min Li, Kaiqi Huang and Tieniu Tan
National Laboratory of Pattern Recognition,Institute of Automation, Chinese Academy of Sciences
Reporter: Chia-Hao HsiehDate: 2009/11/3
CVPR 2008
Outline
• Introduction• Methods– Extract foreground– Estimate vanishing points– Auto-calibration
• Experimental results
Introduction
• Recover intrinsic and extrinsic parameters of cameras– Based on appearance and motion of objects– Measure the camera height only
Practical Camera Auto-Calibration Based on Object Appearance and Motion for Traffic Scene Visual Surveillance
Motion Detection
• Disadvantages of Gaussian Mixture Model – Fast illumination changes– Shadows
• Methods deal with the disadvantages– Model each pixel as the product of irradiance
component and reflectance component– Model each reflectance component as a mixture
of Gaussian
Outline
• Introduction• Methods– Extract foreground– Estimate vanishing points– Auto-calibration
• Experimental results
Vanishing Points Estimation
• Helpful general properties– Moving objects move on the ground plane– Vehicles run along the straight roadway– Vehicles are rich in line segments along two
orientations– Pedestrians walk with their trunks perpendicular
to the ground plane
Vanishing Points Estimation
• Coarse Moving Object Classification– Two directions• Velocity direction • Main axis direction
– Difference of direction• K-Mean clustering• Thresholding
θ < 5°
θ > 20°
from moment analysis of silhouette
Vanishing Points Estimation
• Line Equations EstimationVehicles are rich in line segments along two orientations
– Histogram of Orientated Gradient
Vanishing Points Estimation
• Intersection Estimation– Least square– Levenberg-Marquardt– RANSAC
– But… more and more frames• Voting strategy
– Each point lying on every line generatesa Gaussian impulse in the voting space
Vehicles: 2 vanishing pointsPedestrians: 1 vanishing point
Outline
• Introduction• Methods– Extract foreground– Estimate vanishing points– Auto-calibration
• Experimental results
Camera Calibration
• Recovery of K and R– 3 vanishing points 3 orthogonal directions of
world coordinate system
3 DOFAssume αu = αv = fs = 0
Assume (u0, v0) is on the middle of image plane
1 DO
F
3 DO
F
3 DO
F
Camera Calibration
K and λi solvedSolve R
Camera Calibration• Recovery of T– Choose one arbitrary reference point (u4,v4) from image
plane to correspond to the origin of the world coordinate system
Camera height H is measured
The optical center of the camera lies on the z = H plane
Propose a method of complete calibration of surveillance scenes with three estimated orthogonal vanishing points and the measured camera height H
Experimental Results
720 × 576 (u1, v1) = (−217, 70)(u2, v2) = (1806, 31)(u3, v3) = (427, 4906)
αu = αv = 884,(u0, v0) = (336, 226)
vary in a small range less than 2%
Conclusions
• Practical camera auto-calibration method for traffic scene surveillance
• Completely recover both intrinsic and extrinsic parameters of cameras– Only the camera height H measured– Based on appearance and motion of moving
objects in videos• Accuracy and practicability
Thank you!