Estimation of camera parameters from image sequence for model-based video coding

The authors describe a method for estimating camera parameters from image sequences for application to emerging model-based video coding systems. The parameters to be estimated include focal length, zoom, and 3-D rotation parameters. The method consists of first establishing a correspondence and then, estimating the parameters by fitting the correspondence data to a transformation model based on a perspective mapping model and a 3-D rotation and zoom operation model. They show by simulations and experiments that the proposed method successfully estimates the focal length from the image sequences, it explains very well the induced motion field of images undergoing camera operation (3-D rotation and zoom), and that it significantly outperforms conventional estimation methods, especially for wide-angled images. It is anticipated that the proposed method will be successfully applied to compensating for the motion field induced by camera operation in extracting a 3-D object model and a 3-D object motion, and to synchronizing the viewing direction and scale of an image, in model-based video coding technology