Accurate 3D Multi-marker Tracking in X-ray Cardiac Sequences Using a Two-Stage Graph Modeling Approach

  • Xiaoyan Jiang
  • Daniel Haase
  • Marco Körner
  • Wolfgang Bothe
  • Joachim Denzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8048)


The in-depth analysis of heart movements under varying conditions is an important problem of cardiac surgery. To reveal the movement of relevant muscular parts, biplanar X-ray recordings of implanted radio-opaque markers are acquired. As manually locating these markers in the images is a very time-consuming task, our goal is to automate this process. Taking into account the difficulties in the recorded data such as missing detections or 2D occlusions, we propose a two-stage graph-based approach for both 3D tracklet and 3D track generation. In the first stage of our approach, we construct a directed acyclic graph of 3D observations to obtain tracklets via shortest path optimization. Afterwards, full tracks are extracted from a tracklet graph in a similar manner. This results in a globally optimal linking of detections and tracklets, while providing a flexible framework which can easily be adapted to various tracking scenarios based on the edge cost functions. We validate our approach on an X-ray sequence of a beating sheep heart based on manually labeled ground-truth marker positions. The results show that the performance of our method is comparable to human experts, while standard 3D tracking approaches such as particle filters are outperformed.


Multiple object tracking Directed acyclic graph Min-cost optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andriyenko, A., Schindler, K.: Multi-target tracking by continuous energy minimization. In: CVPR, pp. 1265–1272 (2011)Google Scholar
  2. 2.
    Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. TPAMI 33, 1806–1819 (2011)CrossRefGoogle Scholar
  3. 3.
    Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: The clear mot metrics. EJIVP 246–309 (2008)Google Scholar
  4. 4.
    Bredereck, M., Jiang, X., Körner, M., Denzler, J.: Data association for multi-object tracking-by-detection in multi-camera networks. In: ICDSC (2012)Google Scholar
  5. 5.
    Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.: Online muti-person tracking-by-detection from a single, uncalibrated camera. TPAMI 33(9), 1820–1833 (2011)CrossRefGoogle Scholar
  6. 6.
    Collins, R.T.: Multitarget data association with higher-order motion models. In: CVPR, pp. 744–751 (2012)Google Scholar
  7. 7.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multi-camera people tracking with a probabilistic occupancy map. TPAMI 30, 267–282 (2008)CrossRefGoogle Scholar
  9. 9.
    Ge, W., Collins, R.T.: Multi-target data association by tracklets with unsupervised parameter estimation. In: BMVC (2008)Google Scholar
  10. 10.
    Huang, C., Wu, B., Nevatia, R.: Robust object tracking by hierarchical association of detection responses. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 788–801. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Jiang, X., Rodner, E., Denzler, J.: Multi-person tracking-by-detection based on calibrated multi-camera systems. In: ICCVG, pp. 743–751 (2012)Google Scholar
  12. 12.
    Leal-Taixé, L., Pons-Moll, G., Rosenhahn, B.: Branch-and-price global optimization for multi-view multi-target tracking. In: CVPR, pp. 1987–1994 (2012)Google Scholar
  13. 13.
    Malassiotis, S., Strintzis, M.G.: Tracking the left ventricle in echocardiographic images by learning heart dynamics. IEEE Trans. on Med. Imag. 18, 282–290 (1999)CrossRefGoogle Scholar
  14. 14.
    Muijtjens, A., Roos, J., Arts, T., Hasman, A., Reneman, R.: Tracking markers with missing data by lower rank approximation. J. Biomech. 30, 95–98 (1997)CrossRefGoogle Scholar
  15. 15.
    Nillius, P., Sullivan, J., Carlsson, S.: Multi-target tracking - linking identities using bayesian network inference. In: CVPR, pp. 2187–2194 (2006)Google Scholar
  16. 16.
    Prokaj, J., Duchaineau, M., Medioni, G.: Inferring tracklets for multi-object tracking. In: CVPR Workshops, pp. 37–44 (2011)Google Scholar
  17. 17.
    Satoh, Y., Okatani, T., Deguchi, K.: A color-based tracking by kalman particle filter. In: ICPR, pp. 502–505 (2004)Google Scholar
  18. 18.
    Wu, Z., Kunz, T.H., Betke, M.: Efficient track linking methods for track graphs using network-flow and set-cover techniques. In: CVPR, pp. 1185–1192 (2011)Google Scholar
  19. 19.
    Xing, J., Ai, H., Lao, S.: Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In: CVPR, pp. 1200–1207 (2009)Google Scholar
  20. 20.
    Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR (2008)Google Scholar
  21. 21.
    Zhang, Z.: A flexible new technique for camera calibration. TPAMI 22(11), 1330–1334 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaoyan Jiang
    • 1
  • Daniel Haase
    • 1
  • Marco Körner
    • 1
  • Wolfgang Bothe
    • 2
  • Joachim Denzler
    • 1
  1. 1.Computer Vision GroupFriedrich Schiller University of JenaGermany
  2. 2.Department of Cardiothoracic SurgeryUniversity Hospital JenaGermany

Personalised recommendations