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Anipose: A toolkit for robust markerless 3D pose estimation.

Pierre Karashchuk¹, Katie L Rupp², Evyn S Dickinson²

¹Neuroscience Graduate Program, University of Washington, Seattle, WA, USA.

|September 30, 2021

Summary

This summary is machine-generated.

Anipose is a new open-source toolkit for markerless 3D pose estimation, enabling accurate animal movement quantification. This tool builds on DeepLabCut, expanding 2D tracking capabilities to 3D for diverse research applications.

Keywords:

3D Drosophila joint rotation behavior camera calibration deep learning markerless tracking neuroscience pose estimation robust tracking visualization

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Area of Science:

Ethology and Biomechanics
Computer Vision and Machine Learning

Background:

Accurate quantification of animal movement is essential for behavioral studies.
Existing 2D markerless tracking methods are limited for animals moving in three dimensions.

Purpose of the Study:

To introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation.
To enable researchers to expand existing 2D tracking setups for 3D analysis.

Main Methods:

Anipose integrates DeepLabCut for 2D tracking and adds a 3D calibration module.
It includes error-filtering, temporal-spatial regularization in triangulation, and a video processing pipeline.
The toolkit was evaluated on calibration objects and various animal models (mice, flies, humans).

Main Results:

Anipose provides accurate 3D pose estimation for diverse species.
Analysis of fly walking kinematics revealed a significant role for joint rotation in motor control.
The toolkit successfully processed large numbers of videos for comprehensive analysis.

Conclusions:

Anipose offers a powerful, accessible solution for markerless 3D pose estimation in biological research.
It facilitates advanced kinematic analysis, leading to new insights into animal motor control.
Open-source availability and documentation promote widespread adoption and further development.