The Camera Tracking problem in Mixed and Augmented Reality (MR/AR) has been a research topic that still
raises considerable interest in the scientific community. The solution to the Camera Tracking problem
serves the MR/AR purpose in order to provide an enhancement to the user’s cognitive perception of the real
world and his/her situational awareness in indoor or outdoor environments and serving mobile or fixed workplaces.
There are a variety of different methods to solve the camera tracking problem, namely by placing
squared, circular, coloured or other type of fiducial markers in the scene, by using acoustic or infrared transceivers, or even by natural feature extraction for texture tracking. This work has started with the hypothesis
that, “a low cost, reliable, easy to use and efficient solution to the problem of Texture Tracking is possible”. We
have provided a novel Texture Tracking solution to address this hypothesis. We have assessed five representative
tracking techniques from the literature, but only two have proven to work in real-time ([Simon02] and
[Kato03]).We have chosen to compare our algorithm favourly with these two representatives Texture Tracking
techniques, clarifying the advance of state of the art brought by our technique. We have developed a method
for the quantitative evaluation of performance and precision metrics of our method, paying special attention in
providing, whenever possible, a comparison with the ARToolkit marker-based tracking system, which can be
considered as the “de facto” tracking standard for the AR/MR scientific community.