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Rigid Object Tracking Algorithms for Low-Cost AR Devices

[+] Author Affiliations
Timothy Garrett, Rafael Radkowski, Carl K. Chang, James Oliver

Iowa State University, Ames, IA

Saverio Debernardis, Michele Fiorentino, Antonio E. Uva

Politecnico di Bari, Bari, Italy

Paper No. DETC2014-35304, pp. V01BT02A043; 10 pages
doi:10.1115/DETC2014-35304
From:
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1B: 34th Computers and Information in Engineering Conference
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4629-2
  • Copyright © 2014 by ASME

abstract

Augmented reality (AR) applications rely on robust and efficient methods for tracking. Tracking methods use a computer-internal representation of the object to track, which can be either sparse or dense representations. Sparse representations use only a limited set of feature points to represent an object to track, whereas dense representations almost mimic the shape of an object. While algorithms performed on sparse representations are faster, dense representations can distinguish multiple objects. The research presented in this paper investigates the feasibility of a dense tracking method for rigid object tracking, which incorporates the both object identification and object tracking steps. We adopted a tracking method that has been developed for the Microsoft Kinect to support single object tracking. The paper describes this method and presents the results. We also compared two different methods for mesh reconstruction in this algorithm. Since meshes are more informative when identifying a rigid object, this comparison indicates which algorithm shows the best performance for this task and guides our future research efforts.

Copyright © 2014 by ASME
Topics: Algorithms

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