Biology

Multi-animal DeepLabCut: markerless pose estimation and robust animal tracking

J. Lauer, M. Alam, et al.

This innovative research showcases a multi-animal pose estimation and tracking system that enhances DeepLabCut. Developed by J. Lauer and colleagues, this system employs advanced CNNs for precise animal pose estimation, facilitating in-depth analysis of complex social interactions among marmosets.

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Index

Abstract

This paper introduces a multi-animal pose estimation and tracking system, extending DeepLabCut. It details the development of powerful CNNs (DLCRNet) for animal pose estimation, a data-driven method for automatic skeleton selection, and robust trackers for long-timescale analysis. The system handles multiple animals, even those with similar appearances, and incorporates both supervised and unsupervised animal identity tracking to enhance performance. The authors provide open-source benchmark datasets and demonstrate the system's application to analyzing complex social interactions in marmosets.

Publisher

Nature Methods

Published On

Apr 12, 2022

Authors

J. Lauer, M.Z. Alam, S. Yang, S. Schneider, T. Nath, W. Ma, G. Feng, M.M. Reimer, C. Dulac, D. Schneider, V.N. Murthy, V. Di Santo, G. Lauder, A. Mathis, M.W. Mathis

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