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

Graving, J. M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B. R., et al. (2019). DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife, 8: e47994. doi:10.7554/eLife.47994.

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 Creators:
Graving, Jacob M.1, Author           
Chae, D., Author
Naik, Hemal1, Author           
Li, Liang1, Author           
Koger, Benjamin1, 2, Author           
Costelloe, Blair R.1, Author           
Couzin, Iain D.1, Author           
Affiliations:
1Department of Collective Behavior, Max Planck Institute of Animal Behavior, Max Planck Society, ou_3054976              
2IMPRS for Organismal Biology, Radolfzell, Seewiesen, Max Planck Institut für Ornithologie, Max Planck Society, ou_3172953              

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 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.

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 Dates: 2019-10-01
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.7554/eLife.47994
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Title: eLife
Source Genre: Journal
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Publ. Info: Cambridge : eLife Sciences Publications
Pages: - Volume / Issue: 8 Sequence Number: e47994 Start / End Page: - Identifier: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X