Object recognition and tracking using Bayesian networks for augmented reality systems

One of the major problems in augmented reality (AR) is tracking and registration of both cameras and objects. These tasks must be done accurately to combine real and rendered scenes. In particular, the initialization of the object tracking often remains manual in most systems. This paper proposes the use of Bayesian networks to perform the object recognition and initialization of the tracking. By recognizing the object, special points are taken and we use this information to create generic markers around the scene. Then, an algorithm for pose estimation based on the AR-toolkit library is used to find the orientation of the real object to allow the registration process for 3D objects.