@inproceedings{0e1c4a89871c40fa9e559ce0dcf431ef,
title = "Trainable, vision-based automated home cage behavioral phenotyping",
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 non-standard mouse strains. From this data, we were able to predict the strain identity of individual mice with high accuracy.",
keywords = "Behavior recognition, Computer vision, Mouse, Phenotyping, Rodent",
author = "Hueihan Jhuang and Estibaliz Garrote and Nicholas Edelman and Tomaso Poggio and Andrew Steele and Thomas Serre",
year = "2011",
doi = "10.1145/1931344.1931377",
language = "English",
isbn = "9781605589268",
series = "ACM International Conference Proceeding Series",
booktitle = "Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10",
note = "7th International Conference on Methods and Techniques in Behavioral Research, MB'10 ; Conference date: 24-08-2010 Through 27-08-2010",
}