Abstract
We introduce a natural feature tracking approach that facilitates the tracking of rigid objects for an on-site assembly assistance system. The tracking system must track multiple circuit boards without added fiducial markers, and they are manipulated by the user. We use a common SIFT feature matching detector enhanced with a probability search. This search estimates how likely a set of query descriptors belongs to a particular object. The method was realized and tested. The results show that the probability search enhanced the identification of different circuit boards.
Chapter PDF
References
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Zhou, F., Duh, H., Billinghurst, M.: Trends in Augmented Reality Tracking Interaction and Display: A Review of Ten Years of ISMAR. In: International Symposium on Mixed and Augmented Reality (ISMAR 2008), Cambridge, UK, vol. 2008, p. 10 (July 2008)
Lepetit, V., Fua, P.: Keypoint Recognition using Randomized Trees. Transactions on Pattern Analysis and Machine Intelligence 28(9), 1465–1479 (2006)
Klein, G., Murray, D.: Parallel Tracking and Mapping for Small AR Workspaces. In: Proc. International Symposium on Mixed and Augmented Reality (ISMAR 2007, Nara) (2007)
Chen, Z., Li, X.: Tracking based on Natural Feature for Augmented Reality. In: International Coriference on Educational and Information Technology, ICEIT 2010 (2010)
Laganière, R.: OpenCV Computer Vision. Packt-Publishing, Birmingham (2011)
Cagalaban, G., Kim, S.: Multiple Object Tracking in Unprepared Environments Using Combined Feature for Augmented Reality Applications. In: Kim, T.-h., Chang, A.C.-C., Li, M., Rong, C., Patrikakis, C.Z., Ślęzak, D. (eds.) FGCN 2010. Communications in Computer and Information Science, vol. 119, pp. 1–9. Springer, Heidelberg (2010)
Uchiyama, H., Saito, H., Servières, M., Moreau, G.: Camera tracking by online learning of keypoint arrangements using LLAH in augmented reality applications. In: Virtual Reality, vol. 15(2-3), pp. 109–117 (2011)
Gruber, L., Zollmann, S., Wagner, D., Schmalstieg, D., Höllerer, T.: Optimization of Target Objects for Natural Feature Tracking. In: Proc. IAPR/IEEE ICPR (20th International Conference on Pattern Recognition), 2010, Istanbul, Turkey, August 23–26, pp. 3607–3610 (2010)
Gauglitz, S., Höllerer, T., Turk, M.: Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking. International Journal of Computer Vision (IJCV) 94(3), 335–360 (2011)
Webpage of the OpenCV Wiki: last seen: (February 22, 2013), http://opencv.willowgarage.com/wiki/
Silpa-Anan, C., Hartley, R.: Localization using an imagemap. In: Australasian Conference on Robotics and Automation, Australia, December 6 - 8 (2004)
Freidman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3, 209–226 (1977)
Jacob, E.G., O’Rourke, J., Indyk, P.(ed.): Nearest neighbours in high-dimensional spaces. In: Handbook of Discrete and Computational Geometry, 2nd edn. CRC Press (2004)
Muja, M., Lowe, D.G.: Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. In: International Conference on Computer Vision Theory and Applications, VISAPP 2009 (2009)
Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM 24(6), 381–395 (1981)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Radkowski, R., Oliver, J. (2013). Natural Feature Tracking Augmented Reality for On-Site Assembly Assistance Systems. In: Shumaker, R. (eds) Virtual, Augmented and Mixed Reality. Systems and Applications. VAMR 2013. Lecture Notes in Computer Science, vol 8022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39420-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-39420-1_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39419-5
Online ISBN: 978-3-642-39420-1
eBook Packages: Computer ScienceComputer Science (R0)