Paper
Document
Download
Flag content
7

Deep-learning based identification, tracking, pose estimation, and behavior classification of interacting primates and mice in complex environments

7
TipTip
Save
Document
Download
Flag content

Abstract

Abstract The quantification of behaviors of interest from video data is commonly used to study brain function, the effects of pharmacological interventions, and genetic alterations. Existing approaches lack the capability to analyze the behavior of groups of animals in complex environments. We present a novel deep learning architecture for classifying individual and social animal behavior, even in complex environments directly from raw video frames, while requiring no intervention after initial human supervision. Our behavioral classifier is embedded in a pipeline (SIPEC) that performs segmentation, identification, pose-estimation, and classification of complex behavior, outperforming the state of the art. SIPEC successfully recognizes multiple behaviors of freely moving individual mice as well as socially interacting non-human primates in 3D, using data only from simple mono-vision cameras in home-cage setups.

Paper PDF

This paper's license is marked as closed access or non-commercial and cannot be viewed on ResearchHub. Visit the paper's external site.