Trainable, vision-based automated home cage behavioral phenotyping

  • Authors:

  • Hueihan Jhuang;Estibaliz Garrote;Nicholas Edelman;Tomaso Poggio;Andrew Steele;Thomas Serre

  • Affiliations:

  • Massachusetts Institute of Technology, Cambridge, MA;Massachusetts Institute of Technology, Cambridge, MA;Massachusetts Institute of Technology, Cambridge, MA;Massachusetts Institute of Technology, Cambridge, MA;California Institute of Technology, Pasadena, CA;Massachusetts Institute of Technology, Cambridge, MA

  • Venue:
  • Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research
  • Year:
  • 2010

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Abstract

We describe a fully trainable computer vision system enabling the automated analysis of complex mouse behaviors. Our system computes a sequence of feature descriptors for each video sequence and a classifier is used to learn a mapping from these features to behaviors of interest. We collected a very large manually annotated video database of mouse behaviors for training and testing the system. Our system performs on par with human scoring, as measured from the ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home cage behaviors of two standard inbred and two nonstandard mouse strains. From this data, we were able to predict the strain identity of individual mice with high accuracy.