Using vanishing points for camera calibration
International Journal of Computer Vision
Self-calibration from multiple views with a rotating camera
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Self-Calibration of Stationary Cameras
International Journal of Computer Vision
International Journal of Computer Vision - 1998 Marr Prize
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Calibration of Rotating and Zooming Cameras
International Journal of Computer Vision
Camera Self-Calibration: Theory and Experiments
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Autocalibration from Planar Scenes
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Camera Calibration with One-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simple Calibration Without Metric Information Using an Isoceles Trapezoid
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Camera Calibration and Light Source Estimation from Images with Shadows
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Vertical Parallax from Moving Shadows
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Camera Calibration from Two Shadow Trajectories
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
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Using only shadow trajectories of stationary objects in a scene, we demonstrate that a camera can be calibrated robustly. We require at least two vertical objects to be visible in the image casting shadows on the ground plane. Using properties of these cast shadows, the horizon line (or the line at infinity) of the ground plane is robustly estimated. This leads to pole-polar constraints on the image of the absolute conic (IAC). We show that we require fewer images than the existing methods and demonstrate that our method performs well in presence of large noise. We perform experiments with synthetic data and real data captured from live webcams, demonstrating encouraging results.