Feature-Based Scaffolding for Object Tracking

  • Carlos OrriteEmail author
  • Elena Pollo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10255)


This paper aims at the development of a video-based object tracking algorithm capable to achieve stable and reliable results in a complex situation, facing illumination variation, shape and scale change, background clutter, appearance change caused by camera moving, partial occlusions, etc. The proposal is based on modelling the target object at multiple resolutions by an attributed graph at every scale. Barycentric coordinates are an elegant way to transfer the structure information of a triangle in a particular scale to the neighbouring vertices in the graph to a different scale. The tracking is based on two steps: On the one hand a hill climbing approach is followed to track keypoints. On the other hand the structure of the graph is taken into account to filter out false assignments. The tracking process starts in a rough scale and further it is refined in lower scales to finally localize the keypoints.


Tracking Feature descriptors Attribute graph Meanshift 



This work was partially supported by Spanish Grant TIN2013- 45312-R (MINECO), Gobierno de Aragon and the European Social Found.


  1. 1.
    Hietanen, A., Lankinen, J., Buch, A.G., Kämäräinen, J.-K., Küger, N.: A comparison of feature detectors and descriptors for object class matching. Neurocomputing 184, 3–12 (2016)CrossRefGoogle Scholar
  2. 2.
    Artner, N.M., Kropatsch, W.G.: Structural cues in 2D tracking: edge lengths vs. Barycentric coordinates. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8259, pp. 503–511. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41827-3_63 CrossRefGoogle Scholar
  3. 3.
    Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. Int. J. Comput. Vis. 80(2), 189–210 (2008)CrossRefGoogle Scholar
  4. 4.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Takacs, G., Chandrasekhar, V., Tsai, S.S., Chen, D.M., Grzeszczuk, R., Girod, B.: Fast computation of rotation-invariant image features by an approximate radial gradient transform. IEEE Trans. Image Process. 22(8), 2970–2982 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chandrasekhar, V.R., Chen, D.M., Tsai, S.S., Cheung, N.-M., Chen, H., Takacs, G., Reznik, Y., Vedantham, R., Grzeszczuk, R., Bach, J., Girod, B.: The Stanford mobile visual search data set. In: Proceedings of the Second Annual ACM Conference on Multimedia Systems, pp. 117–122 (2011)Google Scholar
  7. 7.
    Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008).
  8. 8.
    Arandjelović, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)Google Scholar
  9. 9.
    Kovesi, P.: RANSACFITFUNDMATRIX fits fundamental matrix using RANSAC (2005).

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Instituto de Investigacion en Ingenieria de AragonUniversity of ZaragozaZaragozaSpain

Personalised recommendations