RT Journal Article
SR Electronic
T1 DeepPoseKit: a software toolkit for fast and robust animal pose estimation using deep learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 620245
DO 10.1101/620245
A1 Jacob M. Graving
A1 Daniel Chae
A1 Hemal Naik
A1 Liang Li
A1 Benjamin Koger
A1 Blair R. Costelloe
A1 Iain D. Couzin
YR 2019
UL http://biorxiv.org/content/early/2019/08/20/620245.abstract
AB Quantitative behavioral measurements are important for answering questions across scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal’s body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2× with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings—including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.