Advertisement

Animal Social Behaviour: A Visual Analysis

  • Ester Martinez-Martin
  • Angel P. del Pobil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8575)

Abstract

Social activities are among the most striking of animal behaviours, providing knowledge about their intelligence, cognition and evolution. However, their observation in the field can be especially arduous. To address this, image processing methods have been developed. However, despite the extensively research on this topic, multiple object tracking still remains a very hard problem due to the wide variety of issues to be overcome (e.g. changes in illumination conditions, stopped colony member, occlusions, etc.). In this paper, we contribute a novel visual tracking application addressing the challenge of detecting and simultaneously tracking hundreds of animals in their habitat. For that, motion is used as primary cue. The system was validated in experiments with laboratory colonies of micro-robots and several example analysis of dewlap lizard’s behaviour.

Keywords

Background Model Colony Member Circular Trajectory Laboratory Coloni Multiple Object Tracking 
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.
    European project leurre (2006), http://leurre.ulb.ac.be
  2. 2.
    Balch, T., Khan, Z., Veloso, M.: Automatically tracking and analyzing the behavior of live insect colonies. In: AGENTS, Montréal, Quebec, Canada (2001)Google Scholar
  3. 3.
    Camazine, S., Deneubourg, J.L., Franks, N., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton Studies in Complexity. Princeton University Press (2001)Google Scholar
  4. 4.
    Caprari, G., Colot, A., Siegwart, R., Halloy, J., Deneubourg, J.L.: Building mixed societies of animals and robots. IEEE Robotics and Automation Magazine 12(2), 58–65 (2005)CrossRefGoogle Scholar
  5. 5.
    Cordeschi, R.: The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics. Kluwer Academic Publishers, Dordrecht (2002)CrossRefGoogle Scholar
  6. 6.
    Correll, N., Sempo, G., de Meneses, Y.L., Halloy, J., Deneubourg, J.L., Martinoli, A.: Swistrack: A tracking tool for multi-unit robotic and biological systems. In: IROS, pp. 2185–2191 (2006)Google Scholar
  7. 7.
    Francesca, G., Brambilla, M., Trianni, V., Dorigo, M., Birattari, M.: Analysing an evolved robotic behaviour using a biological model of collegial decision making. In: Ziemke, T., Balkenius, C., Hallam, J. (eds.) SAB 2012. LNCS, vol. 7426, pp. 381–390. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Kimura, T., Ohashi, M., Okada, R., Ikeno, H.: A new approach for the simultaneous tracking of multiple honeybees for analysis of hive behavior. Apidologie 42, 607–617 (2011)CrossRefGoogle Scholar
  9. 9.
    Liu, H., Pi, W., Zha, H.: Motion detection for multiple moving targets by using an omnidirectional camera. In: IEEE Conf. on Robotics, Intelligent Systems and Signal Processing, Changsha, China, vol. 1, pp. 422–426 (2003)Google Scholar
  10. 10.
    Marcovecchio, D., Stefanazzi, N., Delrieux, C., Maguitman, A., Ferrero, A.: A multiple object tracking system applied to insect behavior. In: CACIC, Argentina (2013)Google Scholar
  11. 11.
    Orabona, F., Metta, G., Sandini, G.: A Proto-object Based Visual Attention Model. In: Paletta, L., Rome, E. (eds.) WAPCV 2007. LNCS (LNAI), vol. 4840, pp. 198–215. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Partan, S., Larco, C., Owens, M.: Wild tree squirrels respond with multisensory enhancement to conspecific robot alarm behaviour. Animal Behaviour 77(5), 1127–1135 (2009)CrossRefGoogle Scholar
  14. 14.
    Pfeifer, R., Bongard, J.: How the body shapes the way we think: a new view of intelligence. The MIT Press, Cambridge (2007)Google Scholar
  15. 15.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: CVPR, pp. 246–252 (1999)Google Scholar
  16. 16.
    Tinbergen, N.: On aims and methods in ethology. Zeitschrift fur Tierpsychologie 20(4), 410–433 (1963)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ester Martinez-Martin
    • 1
  • Angel P. del Pobil
    • 1
  1. 1.Robotic Intelligence Lab (RobInLab)Universitat Jaume-ICastellónSpain

Personalised recommendations