DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
By Jacob M. Graving, Daniel Chae, Hemal Naik, Liang Li, Benjamin Koger, Blair R. Costelloe, Iain D. Couzin. bioRxiv, 2019
474 citations recorded by the source corpus.
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 eZcient 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.
Subfields: Artificial Intelligence, Social Psychology
View Paper: https://www.semanticscholar.org/paper/d6f80067b4d54cce17683b73ee01665dccda9c6b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sara Beery
- Grant Van Horn
- Pietro Perona
- R. Kays
- Sandeep R. Datta
- Alexander Mathis
- Silvia Zuffi
- Kristin Branson
In Social Psychology
Citations in the corpus
- Computational neuroethology: a call to action (2019, background): “…in artificial neural networks allow identifiable points on an animal’s surface (e.g., joints) to be automatically detected from images with minimal human supervision, even when animals interact or are in rich and complex backgrounds (Graving et al., 2019; Mathis et al., 2018; Pereira et al., 2019).”
- Perspectives in machine learning for wildlife conservation (2021, background): “tion, and environment) afforded by laboratory settings facilitated the rapid development of such tools, many of which are now being adopted for use in field studies of free-moving animals in complex natural environments (29, 31).”
- SLEAP: A deep learning system for multi-animal pose tracking (2022, methodology): “This form of implicit modeling of the geometry between body parts is simple and has been employed in animal pose literature previously 6,7 .”