Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
IEEE MultiMedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Pose Estimation from Points or Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Estimation of Camera Pose Using 2D to 3D Corner Correspondence
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Pseudo-linearizing collinearity constraint for accurate pose estimation from a single image
Pattern Recognition Letters
A new method of camera pose estimation using 2D-3D corner correspondence
Pattern Recognition Letters
VR '06 Proceedings of the IEEE conference on Virtual Reality
Point matching as a classification problem for fast and robust object pose estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper studies the inside looking out camera pose estimation for the virtual studio. The camera pose estimation process, the process of estimating a camera's extrinsic parameters, is based on closed-form geometrical approaches which use the benefit of simple corner detection of 3D cubic-like virtual studio landmarks. We first look at the effective parameters of the camera pose estimation process for the virtual studio. Our studies include all characteristic landmark parameters like landmark lengths, landmark corner angles and their installation position errors and some camera parameters like lens focal length and CCD resolution. Through computer simulation we investigate and analyze all these parameters' efficiency in camera extrinsic parameters, including camera rotation and position matrixes. Based on this work, we found that the camera translation vector is affected more than other camera extrinsic parameters because of the noise of effective camera pose estimation parameters. Therefore, we present a novel iterative geometrical noise cancellation method for the closed-form camera pose estimation process. This is based on the collinearity theory that reduces the estimation error of the camera translation vector, which plays a major role in camera extrinsic parameters estimation errors. To validate our method, we test it in a complete virtual studio simulation. Our simulation results show that they are in the same order as those of some commercial systems, such as the BBC and InterSense IS-1200 VisTracker.