Comparison of Natural Feature Descriptors for Rigid-Object Tracking for Real-Time Augmented Reality

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Bermudez, France
Ward, Sheneeka
Diaz, Christian
Radkowski, Rafael
Garrett, Timothy
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Oliver, James
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Mechanical Engineering
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This paper presents a comparison of natural feature descrip- tors for rigid object tracking for augmented reality (AR) applica- tions. AR relies on object tracking in order to identify a physical object and to superimpose virtual object on an object. Natu- ral feature tracking (NFT) is one approach for computer vision- based object tracking. NFT utilizes interest points of a physcial object, represents them as descriptors, and matches the descrip- tors against reference descriptors in order to identify a phsical object to track. In this research, we investigate four different nat- ural feature descriptors (SIFT, SURF, FREAK, ORB) and their capability to track rigid objects. Rigid objects need robust de- scriptors since they need to describe the objects in a 3D space. AR applications are also real-time application, thus, fast feature matching is mandatory. FREAK and ORB are binary descriptors, which promise a higher performance in comparison to SIFT and SURF. We deployed a test in which we match feature descriptors to artificial rigid objects. The results indicate that the SIFT de- scriptor is the most promising solution in our addressed domain, AR-based assembly training.


This proceeding is published as Bermudez, Francely Franco, Christian Santana Diaz, Sheneeka Ward, Rafael Radkowski, Timothy Garrett, and James Oliver. "Comparison of Natural Feature Descriptors for Rigid-Object Tracking for Real-Time Augmented Reality." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. V01BT02A044-V01BT02A044. American Society of Mechanical Engineers, 2014. Posted with permission.

Wed Jan 01 00:00:00 UTC 2014