Detecting Social Actions of Fruit Flies

  • Eyrun Eyjolfsdottir
  • Steve Branson
  • Xavier P. Burgos-Artizzu
  • Eric D. Hoopfer
  • Jonathan Schor
  • David J. Anderson
  • Pietro Perona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)


We describe a system that tracks pairs of fruit flies and automatically detects and classifies their actions. We compare experimentally the value of a frame-level feature representation with the more elaborate notion of ‘bout features’ that capture the structure within actions. Similarly, we compare a simple sliding window classifier architecture with a more sophisticated structured output architecture, and find that window based detectors outperform the much slower structured counterparts, and approach human performance. In addition we test our top performing detector on the CRIM13 mouse dataset, finding that it matches the performance of the best published method. Our Fly-vs-Fly dataset contains 22 hours of video showing pairs of fruit flies engaging in 10 social interactions in three different contexts; it is fully annotated by experts, and published with articulated pose trajectory features.


Confusion Matrix Confusion Matrice Bout Duration Wing Angle Wing Extension 
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.

Supplementary material

978-3-319-10605-2_50_MOESM1_ESM.pdf (2.7 mb)
Electronic Supplementary Material (PDF 2,777 KB)


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Eyrun Eyjolfsdottir
    • 1
  • Steve Branson
    • 1
  • Xavier P. Burgos-Artizzu
    • 1
  • Eric D. Hoopfer
    • 1
  • Jonathan Schor
    • 1
  • David J. Anderson
    • 1
    • 2
  • Pietro Perona
    • 1
  1. 1.California Institute of TechnologyPasadenaUSA
  2. 2.Howard Hughes Medical Institute (HHMI)USA

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