Multi-animal pose estimation, identification and tracking with DeepLabCut
Name
s41592-022-01443-0.pdf
Description
Published version
Size
11.6 MB
Format
Adobe PDF
Checksum (MD5)
f23f397b44562d7b47a129b961d77875
Author(s)
Lauer, Jessy
Zhou, Mu
Ye, Shaokai
Menegas, William
Schneider, Steffen
Nath, Tanmay
Rahman, Mohammed Mostafizur
Di Santo, Valentina
Soberanes, Daniel
Feng, Guoping
Date Issued
2022
Journal
Nature Methods
Publisher
Springer Science and Business Media LLC
Citation
Lauer, Jessy, Zhou, Mu, Ye, Shaokai, Menegas, William, Schneider, Steffen et al. 2022. "Multi-animal pose estimation, identification and tracking with DeepLabCut." Nature Methods, 19 (4).
Version
Final published version
Abstract
AbstractEstimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41592-022-01443-0
