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Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary

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Abstract

The ability to capture detailed interactions among individuals in a social group is foundational to our study of animal behavior and neuroscience. Recent advances in deep learning and computer vision are driving rapid progress in methods that can record the actions and interactions of multiple individuals simultaneously. Many social species, such as birds, however, live deeply embedded in a three-dimensional world. This world introduces additional perceptual challenges such as occlusions, orientation-dependent appearance, large variation in apparent size, and poor sensor coverage for 3D reconstruction, that are not encountered by applications studying animals that move and interact only on 2D planes. Here we introduce a system for studying the behavioral dynamics of a group of songbirds as they move throughout a 3D aviary. We study the complexities that arise when tracking a group of closely interacting animals in three dimensions and introduce a novel dataset for evaluating multi-view trackers. Finally, we analyze captured ethogram data and demonstrate that social context affects the distribution of sequential interactions between birds in the aviary.

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Data and code availability

Data and code will be made publicly available via Google Drive and GitHub.

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Acknowledgements

We are grateful for the help of Henry Korpi, Ana Alonso, Greg Forkin, and Marcelina Martynek for their helpful discussion and many contributions to annotations in the dataset.

Funding

We gratefully acknowledge support through the following grants: National Science Foundation IOS-1557499, National Science Foundation MRI 1626008, National Science Foundation NCS-FO 2124355.

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Author notes

  1. Shiting Xiao and Yufu Wang have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA

    Shiting Xiao, Yufu Wang, Bernd Pfrommer, Kostas Daniilidis & Marc Badger

  2. Department of Biology, University of Pennsylvania, Philadelphia, PA, USA

    Ammon Perkes & Marc Schmidt

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Contributions

MS and KD conceived of the study. AP, BP, and MS constructed the aviary and collected the data. MB, SX, YW, and KD designed the tracking approaches and dataset. MB, SX, and YW developed the tracking and re-ID pipelines. MB and AP prepared the dataset. MB, SX, and YW performed the experiments and created the figures. MB and SX wrote the first draft. MB, SX, YW, MS and KD edited the paper for submission.

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Correspondence to Marc Badger.

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The authors declare no competing or conflicts of interest.

Ethical approval

The aviary and cowbird data collection were approved by the University of Pennsylvania Institutional Animal Care and Use Committee.

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Communicated by Helge Rhodin.

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Xiao, S., Wang, Y., Perkes, A. et al. Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary. Int J Comput Vis 131, 1532–1549 (2023). https://doi.org/10.1007/s11263-023-01768-z

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