ABSTRACT
Feature based tracking approaches become more and more common for Augmented Reality (AR). However, most upcoming AR solutions are designed for mobile devices, in particular for smartphones and tablet computers, lacking sufficient performance for the execution of state-of-the art feature based approaches at interactive frame rates. In this paper we will present our approach significantly increasing the speed of feature based tracking, thus allowing for real-time applications even on mobile devices. Our approach applies a randomized pose initialization, is applicable to any feature detector and does not require any feature appearance attributes, such as descriptors or ferns.
Supplemental Material
- O. Chum and J. Matas. Matching with prosac - progressive sample consensus. In Proc. of CVPR'05 - Volume 1 - Volume 01, pages 220--226, 2005. Google ScholarDigital Library
- P. David, D. Dementhon, R. Duraiswami, and H. Samet. Softposit: Simultaneous pose and correspondence determination. Int. J. Comput. Vision, 59(3):259{--284, Sept. 2004. Google ScholarDigital Library
- I. Gordon and D. G. Lowe. What and where: 3D object recognition with accurate pose. In Toward Category-Level Object Recognition, pages 67--82, 2006.Google ScholarCross Ref
- J. Herling and W. Broll. An adaptive training-free feature tracker for mobile phones. In Proc. of VRST'10, pages 35--42, New York, 2010. ACM. Google ScholarDigital Library
- G. Klein and D. Murray. Parallel tracking and mapping for small ar workspaces. In Proc. of ISMAR '07, pages 1--10, Washington, DC, USA, 2007. Google ScholarDigital Library
- G. Klein and D. Murray. Parallel tracking and mapping on a camera phone. In Proc. of ISMAR '09, pages 83--86, Washington, DC, USA, 2009. Google ScholarDigital Library
- F. Moreno-Noguer, V. Lepetit, and P. Fua. Pose priors for simultaneously solving alignment and correspondence. In Proc. of ECCV '08, 2008. Google ScholarDigital Library
- W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes 3rd Edition: The Art of Scientific Computing. Cambridge, 2007. Google ScholarDigital Library
- A. L. Rodriguez, P. E. Lopez-De-Teruel, and A. Ruiz. Real-time descriptorless feature tracking. In Proc. of ICIAP '09, pages 853--862. Springer-Verlag, 2009. Google ScholarDigital Library
- J. Sanchez-R., J. Ostlund, P. Fua, and F. Moreno-N. Simultaneous pose, correspondence and non-rigid shape. In Proc. of CVPR, pages 1189--1196, 2010.Google Scholar
- E. Serradell, M. Ozuysal, V. Lepetit, P. Fua, and F. Moreno-Noguer. Combining geometric and appearance priors for robust homography estimation. In Proc. of ECCV'10, pages 58--72, 2010. Google ScholarDigital Library
- R. Szeliski. Computer Vision: Algorithms and Applications. Springer, 2010. Google ScholarDigital Library
- D. Wagner, G. Reitmayr, A. Mulloni, T. Drummond, and D. Schmalstieg. Pose tracking from natural features on mobile phones. In Proc. of ISMAR '08, pages 125--134, Washington, DC, USA, 2008. Google ScholarDigital Library
Index Terms
- Random model variation for universal feature tracking
Recommendations
Hand-Eye Camera Calibration with an Optical Tracking System
ICDSC '18: Proceedings of the 12th International Conference on Distributed Smart CamerasThis paper presents a method for hand-eye camera calibration via an optical tracking system (OTS) faciltating robotic applications. The camera pose cannot be directly tracked via the OTS. Because of this, a transformation matrix between a marker-plate ...
An Extended Marker-Based Tracking System for Augmented Reality
WMSVM '10: Proceedings of the 2010 Second International Conference on Modeling, Simulation and Visualization MethodsFiducial marker systems consist of unique patterns mounted in the environment and computer vision algorithms that help automatically find features in digital camera images. They are useful for Augmented Reality (AR), robot navigation, 3D modeling, and ...
Live RGB-D camera tracking for television production studios
Highlights A novel low-cost tool for camera tracking in broadcasting studio environments. Driftless tracking with keyframes. Real-time performance using a GPU. Allows moving actors in the scene while tracking. Comparison with Kinfu. In this work, a real-...
Comments