Fast color fiducial detection and dynamic workspace extension in video see-through self-tracking augmented reality

Abstract

The registration problem is one of the major issues in augmented reality (AR). Fiducial tracking is gaining interest as a solution to this problem in video see-through AR because of the availability of digitized real scenes. There are several AR systems using fiducial tracking, but most of them operate in small desktop workspaces. It is difficult to apply them directly to large scale applications. The wide range of work distance and non-uniform lighting conditions make fiducial detection very difficult. Adding new fiducials requires off-line processing for measuring positions of new fiducials. We propose a fast and robust fiducial detection procedure with carefully designed color fiducials and noise analysis of digitized images. We also present a dynamic workspace extension method with on-line position determination of unknown features. We present a framework for applying AR to large scale applications.