Towards Probabilistic Shape Vision in RoboCup: A Practical Approach

  • Sven Olufs
  • Florian Adolf
  • Ronny Hartanto
  • Paul Plöger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4434)


This paper presents a robust object tracking method using a sparse shape-based object model. Our approach consists of three ingredients namely shapes, a motion model and a sparse (non-binary) subsampling of colours in background and foreground parts based on the shape assumption. The tracking itself is inspired by the idea of having a short-term and a long-term memory. A lost object is ”missed” by the long-term memory when it is no longer recognized by the short-term memory. Moreover, the long-term memory allows to re-detect vanished objects and using their new position as a new initial position for object tracking. The short-term memory is implemented with a new Monte Carlo variant which provides a heuristic to cope with the loss-of-diversity problem. It enables simultaneous tracking of multiple (visually) identical objects. The long-term memory is implemented with a Bayesian Multiple Hypothesis filter. We demonstrate the robustness of our approach with respect to object occlusions and non-Gaussian/non-linear movements of the tracked object. We also show that tracking can be significantly improved by using compensating ego-motion. Our approach is very scalable since one can tune the parameters for a trade-off between precision and computational time.


Monte Carlo Particle Filter Monte Carlo Variant Monte Carlo Localisation Probabilistic Data Association 
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.


  1. 1.
    Bar-Shalom, Y., Fortmann, T.: Tracking and data association. In: Mathematics in science and engineering, 1st edn., vol. 179, Academic Press Inc., London (1988)Google Scholar
  2. 2.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 790–799 (1995)CrossRefGoogle Scholar
  3. 3.
    Collins, R., Liu, Y., Leordeanu, M.: On-line selection of discriminative tracking features. IEEE Transaction on PAMI 27(10), 1631–1643 (2005)Google Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transaction on PAMI 24, 603–619 (2002)Google Scholar
  5. 5.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transaction on PAMI 25, 564–575 (2003)Google Scholar
  6. 6.
    Cox, I., Hingorani, S.: An efficient implementation and evaluation of reid’s mht algorithm for visual tracking. In: ICPR 1994, pp. 437–442 (1994)Google Scholar
  7. 7.
    Doucet, A., Freitag, N., Gordon, N.: Sequential Monte Carlo Methods in Parctise, pp. 4–16. Springer, New York (2001)Google Scholar
  8. 8.
    Hanek, R., Schmitt, T., Buck, S., Beetz, M.: Towards robocup without color labeling. In: RoboCup International Symposium 2002 (2002)Google Scholar
  9. 9.
    Hundelshausen, F., Rojas, R.: Tracking regions. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  11. 11.
    Nummiaro, K., Koller-Meier, E., Gool, L.: An adaptive color-based particle filter. Image and Vision Computing 21(1), 99–110 (2003)CrossRefGoogle Scholar
  12. 12.
    Olufs, S.: Realtime color-segmentaion of fast moving objects (in German). Master’s thesis, University of Applied Sciences Bonn-Rhein-Sieg (2002)Google Scholar
  13. 13.
    Prez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Reid, D.: An algorithm for tracking multiple targets. IEEE Transaction on Automatic Control 24(6), 843–854 (1979)CrossRefGoogle Scholar
  15. 15.
    Schulz, D., Burgard, W., Fox, D., Cremers, A.: People tracking with a mobile robot using sample-based joint probabilistic data association filters. Journal of Robotics Research (IJRR) (2003)Google Scholar
  16. 16.
    Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust monte carlo localization for mobile robots. Artificial Intelligence 128(1-2), 99–141 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sven Olufs
    • 1
  • Florian Adolf
    • 1
  • Ronny Hartanto
    • 1
  • Paul Plöger
    • 1
  1. 1.Department of Computer Science, Bonn-Rhein-Sieg University of Applied Sciences, D-53757 St. AugustinGermany

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