| Summary: | Augmented Reality (AR) is a technology that aims to embed virtual objects
in the real world, showing to the user the set of objects (virtual and real) as
a single world. For that purpose, it is necessary to offer a perfect alignment
between virtual and real objects, which increases the effectiveness of AR. The
solution to this problem is known as tracking. The object tracking consists in
determining at any time the position and orientation of the camera relative to
the scene. Optical sensors are most commonly used to overcome the tracking
problem due to their low cost implementation. However, it is often difficult to
provide robustness, accuracy and low computational cost at the same time.
This thesis tackles the improvement and development of the main optical
tracking techniques, primarily focused on detecting the deformations of the
bodies. First, it has been achieved the tracking of rigid and non-rigid planar
surfaces through a monocular camera, and then, the object deformation estimation
with a more complex device as a RGB-D camera has been developed.
Surface tracking systems such as those based on markers have the problem
of not being able to handle occlusions. Thus, this thesis raises a new marker
design that offers robustness against occlusions. Furthermore, in order to handle
the deformations of surfaces, a solution that recovers the camera pose and the
non-rigid surface simultaneously is proposed. Continuing with the deformation
handling, it has also developed a robust tracking system for reconstructing the
3D shape of deformable objects using two different physical formulations. One
offers a correct physical behaviour with a low computational cost, whereas the
other achieves higher levels of accuracy at the expense of higher processing
time.
In addition, all the presented solutions have the common factor that all are
executed in real time, which is a key property for a fluently visual feedback of
an AR application.
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