Mechanical Engineering, Materials Science and Engineering, Electrical and Computer Engineering, Human Computer Interaction, Virtual Reality Applications Center
ASME 2014 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
Journal or Book Title
Proceedings of the ASME 2014 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
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.
Bermudez, France Franco; Ward, Sheneeka; Diaz, Christian Santana; Radkowski, Rafael; Garrett, Timothy; and Oliver, James H., "Comparison of Natural Feature Descriptors for Rigid-Object Tracking for Real-Time Augmented Reality" (2014). Mechanical Engineering Conference Presentations, Papers, and Proceedings. 188.