Self-Calibration of Stationary Cameras
International Journal of Computer Vision
International Journal of Computer Vision - 1998 Marr Prize
Geometric Camera Calibration Using Circular Control Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autocalibration from Planar Scenes
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 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
Dissecting the Image of the Absolute Conic
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Camera calibration and light source orientation from solar shadows
Computer Vision and Image Understanding
Multimodal location estimation
Proceedings of the international conference on Multimedia
Multimodal location estimation on Flickr videos
WSM '11 Proceedings of the 3rd ACM SIGMM international workshop on Social media
Estimating the Natural Illumination Conditions from a Single Outdoor Image
International Journal of Computer Vision
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
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Using only shadow trajectories of stationary objects in a scene, we demonstrate that using a set of six or more photographs are sufficient to accurately calibrate the camera. Moreover, we present a novel application where, using only three points from the shadow trajectory of the objects, one can accurately determine the geo-location of the camera, up to a longitude ambiguity, and also the date of image acquisition without using any GPS or other special instruments. We refer to this as "geo-temporal localization". We consider possible cases where ambiguities can be removed if additional information is available. Our method does not require any knowledge of the date or the time when the pictures are taken, and geo-temporal information is recovered directly from the images. We demonstrate the accuracy of our technique for both steps of calibration and geo-temporal localization using synthetic and real data.