Pinhole Camera Calibration in the Presence of Human Noise

The research work presented in this thesis is concerned with the analysis of the human body as a calibration platform for estimation of a pinhole camera model used in Augmented Reality environments mediated through Optical See-Through Head-Mounted Display. Since the quality of the calibration ultimately depends on a subject’s ability to construct visual alignments, the research effort is initially centered around user studies investigating human-induced noise, such as postural sway and head aiming precision. Knowledge about subject behavior is then applied to a sensitivity analysis in which simulations are used to determine the impact of user noise on camera parameter estimation. Quantitative evaluation of the calibration procedure is challenging since the current state of the technology does not permit access to the user’s view and measurements in the image plane as seen by the user. In an attempt to circumvent this problem, researchers have previously placed a camera in the eye socket of a mannequin, and performed both calibration and evaluation using the auxiliary signal from the camera. However, such a method does not reflect the impact of human noise during the calibration stage, and the calibration is not transferable to a human as the eyepoint of the mannequin and the intended user may not coincide. The experiments performed in this thesis use human subjects for all stages of calibration and evaluation. Moreover, some of the measurable camera parameters are verified with an external reference, addressing not only calibration precision, but also accuracy.