Skip to main content

Animal Social Behaviour: A Visual Analysis

  • Conference paper
From Animals to Animats 13 (SAB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8575))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. European project leurre (2006), http://leurre.ulb.ac.be

  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. 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. 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)

    Article  Google Scholar 

  5. Cordeschi, R.: The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics. Kluwer Academic Publishers, Dordrecht (2002)

    Book  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  12. Partan, S.: (2009), http://helios.hampshire.edu/~srpCS/Home.html

  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)

    Article  Google Scholar 

  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. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: CVPR, pp. 246–252 (1999)

    Google Scholar 

  16. Tinbergen, N.: On aims and methods in ethology. Zeitschrift fur Tierpsychologie 20(4), 410–433 (1963)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Martinez-Martin, E., del Pobil, A.P. (2014). Animal Social Behaviour: A Visual Analysis. In: del Pobil, A.P., Chinellato, E., Martinez-Martin, E., Hallam, J., Cervera, E., Morales, A. (eds) From Animals to Animats 13. SAB 2014. Lecture Notes in Computer Science(), vol 8575. Springer, Cham. https://doi.org/10.1007/978-3-319-08864-8_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08864-8_31

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08863-1

  • Online ISBN: 978-3-319-08864-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics