Camera calibration and light source orientation from solar shadows
Computer Vision and Image Understanding
Euclidean path modeling for video surveillance
Image and Vision Computing
Putting Objects in Perspective
International Journal of Computer Vision
Single view geometry and active camera networks made easy
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Intelligent Video for Protecting Crowded Sports Venues
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Robust self-calibration from single image using RANSAC
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Exploiting distinctive visual landmark maps in pan-tilt-zoom camera networks
Computer Vision and Image Understanding
From single cameras to the camera network: an auto-calibration framework for surveillance
Proceedings of the 32nd DAGM conference on Pattern recognition
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
People watching: human actions as a cue for single view geometry
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Multiple ground plane estimation for 3D scene understanding using a monocular camera
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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In the context of visual surveillance of human activity, knowledge about a camera驴s internal and external parameters is useful, as it allows for the establishment of a connection between image and world measurements. Unfortunately, calibration information is rarely available and difficult to obtain after a surveillance system has been installed. In this paper a method for camera autocalibration based on information gathered by tracking people is developed. It brings two main contributions: first, we show how a foot-to-head plane homology can be used to obtain the calibration parameters and then we show an approach how to efficiently estimate initial parameter estimates from measurements; second, we present a Bayesian solution to the calibration problem that can elegantly handle measurement uncertainties, outliers, as well as prior information. It is shown how the full posterior distribution of calibration parameters given the measurements can be estimated, which allows making statements about the accuracy of both the calibration parameters and the measurements involving them.