3D Tracking of Honeybees Enhanced by Environmental Context

  • Guillaume Chiron
  • Petra Gomez-Krämer
  • Michel Ménard
  • Fabrice Requier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


This paper summarizes an approach based on stereo vision to recover honeybee trajectories in 3D at the beehive entrance. The 3D advantage offered by stereo vision is crucial to overcome the rough constraints of the application (number of bees, target dynamics and light). Biologists have highlighted the close scale influence of the environment on bees dynamics. We propose to transpose this idea to enhance our tracking process based on Global Nearest Neighbors. Our method normalizes track/observation association costs that are originally not uniformly distributed over the scene. Therefore, the structure of the scene is needed in order to compute relative distances with the targets. The beehive and especially the flight board is the referent environment for bees, so we propose a method to reconstruct the flight board surface from the noisy and incomplete disparity maps provided by the stereo camera.


stereo vision honeybees 3D tracking surface reconstruction beehive monitoring 


  1. 1.
    Balch, T., Khan, Z., Veloso, M.: Automatically tracking and analyzing the behavior of live insect colonies. In: 5th International Conference on Autonomous Agents, vol. 2001, pp. 521–528. ACM (2001)Google Scholar
  2. 2.
    Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House Radar Library, Artech House (1999)Google Scholar
  3. 3.
    Campbell, J., Mummert, L., Sukthankar, R.: Video monitoring of honey bee colonies at the hive entrance. In: VAIB 2008, vol. 8, pp. 1–4 (2008)Google Scholar
  4. 4.
    Chiron, G., Gomez-Krämer, P., Ménard, M.: Outdoor 3d acquisition system for small and fast targets. application to honeybee monitoring at the beehive entrance. In: GEODIFF 2013, pp. 10–19 (2013)Google Scholar
  5. 5.
    Cleveland, W.S.: LOWESS: A Program for Smoothing Scatterplots by Robust Locally Weighted Regression. The American Statistician 35(1), 54–54 (1981)CrossRefGoogle Scholar
  6. 6.
    Hendriks, C., Yu, Z., Lecocq, A., Bakker, T., Locke, B., Terenius, O.: Identifying all individuals in a honeybee hive - progress towards mapping all social interactions. In: VAIB 2012 (2012)Google Scholar
  7. 7.
    Henry, M., Beguin, M., Requier, F., Rollin, O., Odoux, J.F., Aupinel, P., Aptel, J., Tchamitchian, S., Decourtye, A.: A common pesticide decreases foraging success and survival in honey bees. Science 336(6079), 348–350 (2012)CrossRefGoogle Scholar
  8. 8.
    Kalman, R.E., et al.: A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82(1), 35–45 (1960)CrossRefGoogle Scholar
  9. 9.
    Khan, Z., Balch, T., Dellaert, F.: Efficient particle filter-based tracking of multiple interacting targets using an mrf-based motion model. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 254–259. IEEE (2003)Google Scholar
  10. 10.
    Khan, Z., Balch, T., Dellaert, F.: A rao-blackwellized particle filter for eigentracking. In: Computer Vision and Pattern Recognition, vol. 2, pp. II–980 (2004)Google Scholar
  11. 11.
    Kimura, T., Ohashi, M., Okada, R., et al.: A new approach for the simultaneous tracking of multiple honeybees for analysis of hive behavior. Apidologie 42(5), 607–617 (2011)CrossRefGoogle Scholar
  12. 12.
    Maitra, P., Schneider, S., Shin, M.: Robust bee tracking with adaptive appearance template and geometry-constrained resampling. In: 2009 Workshop on Applications of Computer Vision (WACV), pp. 1–6. IEEE (2009)Google Scholar
  13. 13.
    Miranda, B., Salas, J., Vera, P.: Bumblebees detection and tracking. In: VAIB 2012 (2012)Google Scholar
  14. 14.
    Portelli, G., Ruffier, F., Roubieu, F.L., et al.: Honeybees’ speed depends on dorsal as well as lateral, ventral and frontal optic flows. PLoS ONE 6(5), e19486 (2011)Google Scholar
  15. 15.
    Ristič, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House Radar Library, Artech House (2004)Google Scholar
  16. 16.
    Theriault, D., Wu, Z., Hristov, N., Swartz, S., Breuer, K., Kunz, T., Betke, M.: Reconstruction and analysis of 3d trajectories of brazilian free-tailed bats in flight. Tech. rep., CS Department, Boston University (2010)Google Scholar
  17. 17.
    Veeraraghavan, A., Chellappa, R., Srinivasan, M.: Shape-and-behavior encoded tracking of bee dances. IEEE Transactions on PAMI 30(3), 463–476 (2008)CrossRefGoogle Scholar
  18. 18.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, vol. 1, pp. I–511 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guillaume Chiron
    • 1
  • Petra Gomez-Krämer
    • 1
  • Michel Ménard
    • 1
  • Fabrice Requier
    • 2
    • 3
  1. 1.L3I, University of La RochelleLa RochelleFrance
  2. 2.INRA, UE 1255UE EntomologieSurgèresFrance
  3. 3.CNRS, UPR 1934CEBCBeauvoir sur NiortFrance

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