Hybrid Feature and Template Based Tracking for Augmented Reality Application

  • Gede Putra Kusuma NegaraEmail author
  • Fong Wee Teck
  • Li Yiqun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9010)


Visual tracking is the core technology that enables the vision-based augmented reality application. Recent contributions in visual tracking are dominated by template-based tracking approaches such as ESM due to its accuracy in estimating the camera pose. However, it is shown that the template-based tracking approach is less robust against large inter-frames displacements and image variations than the feature-based tracking. Therefore, we propose to combine the feature-based and template-based tracking into a hybrid tracking model to improve the overall tracking performance. The feature-based tracking is performed prior to the template-based tracking. The feature-based tracking estimates pose changes between frames using the tracked feature-points. The template-based tracking is then used to refine the estimated pose. As a result, the hybrid tracking approach is robust against large inter-frames displacements and image variations. It also accurately estimates the camera pose. Furthermore, we will show that the pose adjustment performed by the feature-based tracking reduces the number of iterations necessary for the ESM to refine the estimated pose.


Image Sequence Reference Image Visual Tracking Feature Tracker Image Variation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

Supplementary material (avi 2,235 KB)


  1. 1.
    Lourakis, M.I.A.: homest: A c/c++ library for robust, non-linear homography estimation, July 2006. Accessed on 17 Dec 2011
  2. 2.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, New York (2003)Google Scholar
  3. 3.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  4. 4.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  5. 5.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)Google Scholar
  6. 6.
    Benhimane, S., Malis, E.: Homography-based 2d visual tracking and servoing. Int. J. Robot. Res. 26, 661–676 (2007)CrossRefGoogle Scholar
  7. 7.
    Fong, W.T., Ong, S.K., Nee, A.Y.C.: Computer vision centric hybrid tracking for augmented reality in outdoor urban environments. In: Proceedings of the International Conference on Virtual Reality Continuum and its Applications in Industry, VRCAI 2009, pp. 185–190. ACM, New York (2009)Google Scholar
  8. 8.
    Ladikos, A., Benhimane, S., Navab, N.: A real-time tracking system combining template-based and feature-based approaches. In: VISAPP, pp. 325–332 (2007)Google Scholar
  9. 9.
    Kusuma, G.P., Szabo, A., Li, Y., Lee, J.A.: Appearance-based object recognition using weighted longest increasing subsequence. In: Proceedings of the International Conference on Pattern Recognition, Tsukuba, Japan, pp. 3668–3671 (2012)Google Scholar
  10. 10.
    Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18, 509–517 (1975)CrossRefzbMATHGoogle Scholar
  11. 11.
    Fredman, M.L.: On computing the length of longest increasing subsequences. Discrete Math. 11, 29–35 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Ma, Y., Soatto, S., Kosecka, J., Sastry, S.S.: An Invitation to 3-D Vision: From Images to Geometric Models. Springer, New York (2003)Google Scholar
  13. 13.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000)CrossRefGoogle Scholar
  14. 14.
    Bouguet, J.Y.: Pyramidal implementation of the lucas kanade feature tracker. Intel Corporation, Microprocessor Research Labs (2000)Google Scholar
  15. 15.
    Lieberknecht, S., Benhimane, S., Meier, P., Navab, N.: A dataset and evaluation methodology for template-based tracking algorithms. In: Proceedings of IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2009, pp. 145–151. IEEE Computer Society, Washington, DC (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gede Putra Kusuma Negara
    • 1
    Email author
  • Fong Wee Teck
    • 1
  • Li Yiqun
    • 1
  1. 1.Visual Computing DepartmentInstitute for Infocomm ResearchSingaporeSingapore

Personalised recommendations