SWARMTRACK: A PARTICLE SWARM APPROACH TO VISUAL TRACKING

Luis Antón-Canalís, Elena Sánchez-Nielsen, Mario Hernández-Tejera

Abstract

A new approach to solve the object tracking problem is proposed using a Swarm Intelligence metaphor. It is based on a prey-predator scheme with a swarm of predator particles defined to track a herd of prey pixels using the intensity of its flavours. The method is described, including the definition of predator particles’ behaviour as a set of rules in a Boids fashion. Object tracking behaviour emerges from the interaction of individual particles. The paper includes experimental evaluations with video streams that illustrate the robustness and efficiency for real-time vision based tasks using a general purpose computer.

References

  1. Aloimonos, Y., 1993. Active Perception. Lawrence Erlbaum Assoc., Pub., N.J.
  2. Belongie, S., Malik, J., and Puzicha J., 2002. Shape matching and object recognition using shape context. In IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(4):509-522.
  3. Besh, P. J., and McKay N., 1992. A method for registration of 3D shapes. In IEEE Trans. on Pattern Analysis and Machine Intelligence, 14(2):239-256.
  4. Blake, A., Curwen R., and Zisserman A., 1993. A framework for spatio-temporal control in the tracking of visual contours. In International Journal of Computer Vision, 11(2):127-145.
  5. Bonabeau, E., Dorigo, M., and Theraulaz, G., 2000. Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press.
  6. Chen Y. amd Medioni G., 1992. Object modelling by registration of multiple range images. In Image and Vision Computing, 10(3):145-155.
  7. Comaniciu, D., Ramesh V., and Meer P., 2000. Real-time tracking of non-rigid objects using mean shift. In IEEE Conf. on Computer Vision and Pattern Recognition, volume II, pp. 142-149, Hilton Head, SC.
  8. Dorigo M., V. Maniezzo & A. Colorni, 1996. Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26(1):29-41.
  9. Eberhart, R. C. and Kennedy, J., 1995. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan. pp. 39-43.
  10. Guerra Cayetano, Hernández Mario, Domínguez Antonio, Hernández Daniel, 2005. A new approach to the template update problem. In Lecture Notes in Computer Science LNCS 3522, pp. 217-224.
  11. Isard, M., and Blake A., 1998. Condensation-conditional density propagation for visual tracking. In International Journal of Computer Vision, 29(1):5-28.
  12. Kass, M., Witkin A., and Terzopoulos D. Snakes: active contour models. In Proc. 1st International Conference on Computer Vision.
  13. Kennedy, J. and Eberhart, R. C., 1995. Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ. pp. 1942-1948.
  14. Matthews, I., Ishikawa, T., and Baker S., 2004. The template update problem. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6):810- 815.
  15. Parra, C., Murrieta-Cid, Devy, M., and Briot, M., 1999. 3-D Modelling and Robot Localization from Visual and Range Data in Natural Scenes. In Lecture Notes in Computer Science 1542, Springer Verlag, pp. 450-468.
  16. Parrish, Julia K. (Editor), William M. Hamner (Editor), 1997. Animal Groups in Three Dimensions: How Species Aggregate. Cambridge University Press.
  17. Pentland, A., 2000. Perceptual Intelligence. In Communications of ACM, 43(3):35-44.
  18. Reynolds, C. W., 1987. Flocks, Herds, and Schools: A Distributed Behavioural Model. In Computer Graphics, 21(4) (SIGGRAPH 7887 Conference Proceedings) pp. 25-34.
  19. Reynolds, J., 1998. Autonomous underwater vehicle: vision system. PhD thesis, Robotic Systems Lab. Department of Engineering. Australian National University Canberra, Australia.
  20. Rucklidge W. J., 1996. Efficient Visual Recognition Using the Hausdorff Distance. In Lecture Notes in Computer Science, nº 1173, Springer-Verlag, NY.
  21. Sánchez-Nielsen Elena, Hernández-Tejera Mario, 2005a. A fast and accurate tracking approach for automated visual surveillance. In 39th IEEE International Carnahan Conference on Security Technology, pp. 113-116.
  22. Sánchez-Nielsen Elena, Hernández-Tejera Mario, 2005b. A heuristic search based approach for moving objects tracking. In 19th International Joint Conference on Artificial Intelligence (IJCAI-05), pp. 1736-1737.
  23. Turk, M., 2004. Computer Vision in the Interface. In Communications of the ACM, 47(1): 61-67.
  24. Tyng-Luh Liu, Hwan-Tzong Chen, 2004. Real-Time tracking using trust-region methods. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):397-401.
  25. Yuille, A., Hallinan, P., and Cohen D., 1992. Feature extraction from faces using deformable templates. In International Journal of Computer Vision 8(2):99-112.
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Paper Citation


in Harvard Style

Antón-Canalís L., Sánchez-Nielsen E. and Hernández-Tejera M. (2006). SWARMTRACK: A PARTICLE SWARM APPROACH TO VISUAL TRACKING . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 972-8865-40-6, pages 221-228. DOI: 10.5220/0001372002210228


in Bibtex Style

@conference{visapp06,
author={Luis Antón-Canalís and Elena Sánchez-Nielsen and Mario Hernández-Tejera},
title={SWARMTRACK: A PARTICLE SWARM APPROACH TO VISUAL TRACKING},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2006},
pages={221-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001372002210228},
isbn={972-8865-40-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - SWARMTRACK: A PARTICLE SWARM APPROACH TO VISUAL TRACKING
SN - 972-8865-40-6
AU - Antón-Canalís L.
AU - Sánchez-Nielsen E.
AU - Hernández-Tejera M.
PY - 2006
SP - 221
EP - 228
DO - 10.5220/0001372002210228