DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels.

ABSTRACT: Videos of animal behavior are used to quantify researcher-defined behaviors of interest to study neural function, gene mutations, and pharmacological therapies. Behaviors of interest are often scored manually, which is time-consuming, limited to few behaviors, and variable across researchers. We created DeepEthogram: software that uses supervised machine learning to convert raw video pixels into an ethogram, the behaviors of interest present in each video frame. DeepEthogram is designed to be general-purpose and applicable across species, behaviors, and video-recording hardware. It uses convolutional neural networks to compute motion, extract features from motion and images, and classify features into behaviors. Behaviors are classified with above 90% accuracy on single frames in videos of

SUBMITTER: Bohnslav JP 

PROVIDER: S-EPMC8455138 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature