Optimality of black to white ratio and information complexity for robust marker recognition

Abstract

Fiducial markers are widely used in many Augmented Reality (AR) applications such as to manipulate AR objects, robot navigation, education and AR based games. ARToolKit is an open source, easy to configure and well-documented tracking system which is widely used for designing marker based AR applications. The quality of ARToolKit markers plays a vital role in the performance of these AR applications, but currently there is no algorithm nor quantitative measure to guide users for designing high quality markers in order to achieve reliable tracking. In this paper we experimentally analyze the effect of the two marker's design attributes, including black to white ratio (B/W) and information complexity on marker recognition. Based on above analysis, we developed threshold based algorithms for optimizing the two attributes. These algorithms also provide various cognitive aids and helps to the users. The work done will help ARToolKit users to design high quality markers with little efforts and as a result will increase the efficiency and performance of their AR applications.