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Published August 2010 | public
Book Section - Chapter

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 nonstandard mouse strains. From this data, we were able to predict the strain identity of individual mice with high accuracy.

Additional Information

© 2010 ACM. This research would not have been possible without the dedicated work of our data collectors and annotators: Jessi Ambrose, Andrea Farbe, Cynthia Hsu, lexandra Jiang, Grant Kadokura, Xuying Li, Anjali Patel, Kelsey Von Tish, Emma Tolley, Ye Yao and Eli T Williams. This research was sponsored by grants from DARPA (IPTO and DSO), NSF-0640097, NSF-0827427, IIT, and the McGovern Institute for Brain Research. Andrew Steele was funded by the Broad Fellows Program in Brain Circuitry at Caltech. Hueihan Jhuang was funded by the Taiwan National Science Council (TMS-094-1-A032).

Additional details

Created:
August 19, 2023
Modified:
October 23, 2023