Inside looking out camera pose estimation for virtual studio

• Published in: Graphical models Vol. 70; no. 4; pp. 57 - 75
• Main Authors: Rahbar, Kambiz, Pourreza, Hamid Reza
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01-07-2008; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Closed-form camera pose estimation; Collinearity theory; Computer science; control theory; systems; Control theory. Systems; Exact sciences and technology; Inside looking out; Iterative geometrical noise cancellation; Modelling and identification; Pattern recognition. Digital image processing. Computational geometry; Inside looking out; Iterative geometrical noise cancellation; Collinearity theory; Closed-form camera pose estimation; Estimation error; Parameter estimation; Error estimation; Computer simulation; Noise reduction; Focal length; Television studio; Iterative method; Visual servoing; Modeling; Posture; Exact solution; Virtual reality; Geometrical method; System identification; Signal to noise ratio
• ISSN: 1524-0703; 1524-0711
• DOI: 10.1016/j.gmod.2008.01.001

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.