DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
Abstract
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 >2x 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 01, 2019
- Source ID
- 10.7554/elife.47994
Entities
People
- Benjamin Koger
- Blair R Costelloe
- Daniel Chae
- Hemal Naik
- Iain Couzin
- Jacob M Graving
- Liang Li
Organizations
- Army Research Office
- German Research Foundation
- Horizon 2020
- Max Planck Institute of Animal Behavior
- Max Planck Society
- Ministry of Science, Research and the Arts of Baden-Württemberg
- National Science Foundation
- Nvidia
- Office of Naval Research
- Princeton University
- Technical University of Munich
- University of Konstanz