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Elife. 2019 Oct 1;8. pii: e47994. doi: 10.7554/eLife.47994.

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning.

Graving JM1,2,3, Chae D4, Naik H1,2,3,5, Li L1,2,3, Koger B1,2,3, Costelloe BR1,2,3, Couzin ID1,2,3.

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Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany.
Department of Biology, University of Konstanz, Konstanz, Germany.
Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany.
Department of Computer Science, Princeton University, Princeton, United States.
Chair for Computer Aided Medical Procedures, Technische Universität München, Munich, Germany.


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.


D. melanogaster; Equus grevyi; Grévy's zebra; Schistocerca gregaria; desert locust; neuroscience

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