3 research outputs found
Model-based camera tracking for augmented reality
Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Master's) -- Bilkent University, 2014.Includes bibliographical references leaves 45-49.Augmented reality (AR) is the enhancement of real scenes with virtual entities. It
is used to enhance user experience and interaction in various ways. Educational
applications, architectural visualizations, military training scenarios and pure
entertainment-based applications are often enhanced by augmented reality to
provide more immersive and interactive experience for the users. With hand-held
devices getting more powerful and cheap, such applications are becoming very
popular.
To provide natural AR experiences, extrinsic camera parameters (position
and rotation) must be calculated in an accurate, robust and efficient way so that
virtual entities can be overlaid onto the real environments correctly. Estimating
extrinsic camera parameters in real-time is a challenging task. In most camera
tracking frameworks, visual tracking serve as the main method for estimating
the camera pose. In visual tracking systems, keypoint and edge features are
often used for pose estimation. For rich-textured environments, keypoint-based
methods work quite well and heavily used. Edge-based tracking, on the other
hand, is more preferable when the environment is rich in geometry but has little
or no visible texture.
Pose estimation for edge based tracking systems generally depends on the
control points that are assigned on the model edges. For accurate tracking, visibility
of these control points must be determined in a correct manner. Control
point visibility determination is computationally expensive process. We propose
a method to reduce computational cost of the edge-based tracking by preprocessing
the visibility information of the control points. For that purpose, we use
persistent control points which are generated in the world space during preprocessing
step. Additionally, we use more accurate adaptive projection algorithm for persistent control points to provide more uniform control point distribution
in the screen space.
We test our camera tracker in different environments to show the effectiveness
and performance of the proposed algorithm. The preprocessed visibility information
enables constant time calculations of control point visibility while preserving
the accuracy of the tracker. We demonstrate a sample AR application with user
interaction to present our AR framework, which is developed for a commercially
available and widely used game engine.Aman, AytekM.S