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

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2019
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eLife. eLife Sciences Publications. 2019, 8, e47994. eISSN 2050-084X. Available under: doi: 10.7554/eLife.47994
Zusammenfassung

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.

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570 Biowissenschaften, Biologie
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ISO 690GRAVING, Jacob M., Daniel CHAE, Hemal NAIK, Liang LI, Benjamin KOGER, Blair R. COSTELLOE, Iain D. COUZIN, 2019. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. In: eLife. eLife Sciences Publications. 2019, 8, e47994. eISSN 2050-084X. Available under: doi: 10.7554/eLife.47994
BibTex
@article{Graving2019-10-01DeepP-47135,
  year={2019},
  doi={10.7554/eLife.47994},
  title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning},
  volume={8},
  journal={eLife},
  author={Graving, Jacob M. and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R. and Couzin, Iain D.},
  note={Article Number: e47994}
}
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