Extendible object-centric tracking for augmented reality

Presents a novel object-centric tracking architecture for presenting augmented reality media in spatial relationships to objects, regardless of the objects´ positions or motions in the world. The advance this system provides over previous object-centric tracking approaches is the ability to sense and integrate new features into its tracking database, thereby extending the tracking region automatically. This lazy evaluation of the structure-from-motion problem uses images obtained from a single calibrated moving camera and applies recursive filtering to identify and estimate the 3D positions of new features. We evaluate the performance of two filters; a classic extended Kalman filter (EKF) and a filter based on a recursive average of covariances (RAC). Implementation issues and results are discussed in conclusion.