Automatic Camera Calibration Using Automatic Camera Calibration Using Pattern Detection for Vision-Based Pattern Detection for Vision-Based Speed Sensing Speed Sensing Neeraj K. Kanhere Neeraj K. Kanhere Dr. Stanley T. Birchfield Dr. Stanley T. Birchfield Department of Electrical Engineering Department of Electrical Engineering Dr. Wayne A. Sarasua, P.E. Dr. Wayne A. Sarasua, P.E. Department of Civil Engineering Department of Civil Engineering College of Engineering and College of Engineering and Science Science Clemson University Clemson University
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Automatic Camera Calibration Using Pattern Detection for Vision-Based Speed Sensing Neeraj K. Kanhere Dr. Stanley T. Birchfield Department of Electrical.
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Automatic Camera Calibration Using Pattern Automatic Camera Calibration Using Pattern Detection for Vision-Based Speed SensingDetection for Vision-Based Speed Sensing
Neeraj K. KanhereNeeraj K. KanhereDr. Stanley T. BirchfieldDr. Stanley T. Birchfield
Department of Electrical EngineeringDepartment of Electrical Engineering
Dr. Wayne A. Sarasua, P.E.Dr. Wayne A. Sarasua, P.E.Department of Civil EngineeringDepartment of Civil Engineering
College of Engineering and ScienceCollege of Engineering and ScienceClemson UniversityClemson University
IntroductionIntroduction
Traffic parameters such as volume, speed, and vehicle classification are fundamental for…
Traffic parameters such as volume, speed, and vehicle classification are fundamental for…
Estimation of parameters for the assumed camera model
Direct estimation of projective transform
Goal is to estimate 11 elements of a matrix which transforms points in 3D to a 2D plane
Harder to incorporate scene-specific knowledge
Goal is to estimate camera parameters such as focal length and pose
Easier to incorporate known quantities and constraints
Image-world correspondences
M[3x4] M[3x4]
f, h, Φ, θ …
Manual calibrationManual calibration
Bas and Crisman (1997)Kanhere et al. (2006)
Lai (2000) Fung et al. (2003)
Automatic calibrationAutomatic calibration
Song et al. (2006)Song et al. (2006)
• Known camera heightKnown camera height• Needs background imageNeeds background image• Depends on detecting road Depends on detecting road markingsmarkings
Dailey et al. (2000)Dailey et al. (2000)
Schoepflin and Dailey (2003)Schoepflin and Dailey (2003)
• Avoids calculating camera Avoids calculating camera ParametersParameters• Based on assumptions that Based on assumptions that reduce the problem to 1-D reduce the problem to 1-D geometrygeometry• Uses parameters from the Uses parameters from the distribution of vehicle distribution of vehicle lengths.lengths.
• Uses two vanishing pointsUses two vanishing points• Lane activity map sensitive of spill-over Lane activity map sensitive of spill-over • Correction of lane activity map needs Correction of lane activity map needs background imagebackground image
Lane activity map Peaks at lane centers
Our approach to automatic calibrationOur approach to automatic calibration
Input frameInput frame
BCVD
Tracking data
CorrespondenceCorrespondence
exis
ting
vehi
cles
dete
ctio
nsne
w v
ehic
les TrackingTracking
strong gradients?strong
gradients?
VP-0 Estimation
VP-0 Estimation
VP-1 Estimation
VP-1 Estimation
CalibrationCalibration SpeedsSpeeds
Yes
RANSACRANSAC
Input frameInput frame
BCVD
Tracking data
CorrespondenceCorrespondence
exis
ting
vehi
cles
dete
ctio
nsne
w v
ehic
les TrackingTracking
strong gradients?strong
gradients?
VP-0 Estimation
VP-1 Estimation
VP-1 Estimation
VP-2 Estimation
CalibrationCalibration SpeedsSpeeds
Yes
RANSACRANSAC
• Does not depend on road markings• Does not require scene specific parameters such as lane dimensions• Works in presence of significant spill-over (low height)• Works under night-time condition (no ambient light)
• Does not depend on road markings• Does not require scene specific parameters such as lane dimensions• Works in presence of significant spill-over (low height)• Works under night-time condition (no ambient light)