| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 99 === Augmented Reality (AR) is an innovative technology, overlaying real-world image with 3D virtual objects, to bridge virtual and real worlds.
To overlay the 3D virtual object on a real world object, vision-based object tracking algorithms using natural features are widely used for tracking objects.
Unfortunately, most of the existing algorithms of object tracking are not efficient enough for real world scenarios, nor robust enough to deal with objects with repeated patterns.
In this thesis, we propose a cluster-based algorithm for feature tracking (CRAFT) to efficiently track an object with repeated patterns in different motions.
In our design, the CRAFT algorithm first clusters features derived from the template (object model) and determines the region of each cluster. A projective transformation matrix is then used to locate the corresponding regions in the video frames. Since CRAFT only computes and searches features in the projected regions, it reduces the computational costs and further distinguishes different patterns when recognizing a object with repeated patterns. In this thesis, we conduct several experiments to compare the accuracy and computational performance of the proposed algorithm. Compared with SURF, the experiment results show that CRAFT has consistently excellent accuracy of pose estimation and improves efficiency in recognizing an object by a factor of 2.5.
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