RT Journal Article
SR Electronic
T1 Multi-animal pose estimation and tracking with DeepLabCut
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.30.442096
DO 10.1101/2021.04.30.442096
A1 Jessy Lauer
A1 Mu Zhou
A1 Shaokai Ye
A1 William Menegas
A1 Tanmay Nath
A1 Mohammed Mostafizur Rahman
A1 Valentina Di Santo
A1 Daniel Soberanes
A1 Guoping Feng
A1 Venkatesh N. Murthy
A1 George Lauder
A1 Catherine Dulac
A1 Mackenzie W. Mathis
A1 Alexander Mathis
YR 2021
UL http://biorxiv.org/content/early/2021/04/30/2021.04.30.442096.abstract
AB Estimating 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 extremely similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, a popular open source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for robust multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity directly 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.Competing Interest StatementThe authors have declared no competing interest.