Advertisement

Tracking and Labelling of Interacting Multiple Targets

  • Josephine Sullivan
  • Stefan Carlsson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

Abstract

Successful multi-target tracking requires solving two problems – localize the targets and label their identity. An isolated target’s identity can be unambiguously preserved from one frame to the next. However, for long sequences of many moving targets, like a football game, grouping scenarios will occur in which identity labellings cannot be maintained reliably by using continuity of motion or appearance. This paper describes how to match targets’ identities despite these interactions.

Trajectories of when a target is isolated are found. These trajectories end when targets interact and their labellings cannot be maintained. The interactions (merges and splits) of these trajectories form a graph structure. Appropriate feature vectors summarizing particular qualities of each trajectory are extracted. A clustering procedure based on these feature vectors allows the identities of temporally separated trajectories to be matched. Results are shown from a football match captured by a wide screen system giving a full stationary view of the pitch.

Keywords

Feature Vector Image Gradient Relative Depth Interaction Graph Foreground Pixel 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Khan, Z., Balch, T., Dellaert, F.: An mcmc-based particle filter for tracking multiple interacting targets. In: European Conference on Computer Vision (2004)Google Scholar
  2. 2.
    Gelgon, M., Bouthemy, P., Le Cadre, J.: Recovery of the trajectories of multiple moving objects in an image sequence with a pmht approach. J. Image & Vision Computing 23, 19–31 (2005)CrossRefGoogle Scholar
  3. 3.
    Xu, M., Orwell, J., Jones, G.: Tracking football players with multiple cameras. In: IEEE International Conference on Image Processing (2004)Google Scholar
  4. 4.
    Iwase, S., Saito, H.: Parallel tracking of all soccer players by integrating detected positions in multiple view images. In: ICPR, pp. 751–754 (2004)Google Scholar
  5. 5.
    Vermaak, J., Doucet, A., Perez, P.: Maintaining multi-modality through mixture tracking. In: International Conference on Computer Vision (2003)Google Scholar
  6. 6.
    Okuma, K., Taleghani, A., De Freitas, N., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Needham, C., Boyle, R.: Tracking multiple sports players through occlusion, congestion and scale. In: BMVC (2001)Google Scholar
  8. 8.
    Figueroa, P., Leite, N., Barros, R., Cohen, I., Medioni, G.: Tracking soccer players using the graph representation. In: ICPR, pp. 787–790 (2004)Google Scholar
  9. 9.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Conference on Computer Vision and Pattern Recognition (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Josephine Sullivan
    • 1
  • Stefan Carlsson
    • 1
  1. 1.Royal Institute of TechnologyStockholmSweden

Personalised recommendations