AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild
Open accessAbstract
Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging. Being able to study this incredible agility will be critical for the development of next-generation autonomous legged robots. In particular, the cheetah (acinonyx jubatus) is supremely fast and maneuverable, yet quantifying its wholebody 3D kinematic data during locomotion in the wild remains a challenge, even with new deep learning-based methods. In this work we present an extensive dataset of free-running cheetahs in the wild, called AcinoSet, that contains 119, 490 frames of multi-view synchronized high-speed video footage, camera calibration files and 7, 588 human-annotated frames. We utilize markerless animal pose estimation to provide 2D keypoints. Then, we use three methods that serve as strong baselines for 3D pose estimation tool development: traditional sparse bundle adjustment, an Extended Kalman Filter, and a trajectory optimization-based method we call Full Trajectory Estimation. The resulting 3D trajectories, human-checked 3D ground truth, and an interactive tool to inspect the data is also provided. We believe this dataset will be useful for a diverse range of fields such as ecology, neuroscience, robotics, biomechanics as well as computer vision. Code and data can be found at: https://github.com/African-Robotics-Unit/AcinoSet.
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The high level of control afforded by laboratory settings facilitated the rapid development of such tools, many of which are now being adopted for use in field studies of free-moving animals in complex natural environments.
“The high level of control (e.g., of lighting conditions, sensor calibration, and environment) afforded by laboratory settings facilitated the rapid development of such tools, many of which are now being adopted for use in field studies of free-moving animals in complex natural environments75,142.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [142]
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Animal Identification with Independent Foreground and Background Modeling
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Markerless 3D kinematics and force estimation in cheetahs
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- Authors
- Joska, D., Clark, L., Muramatsu, N., Jericevich, R., Nicolls, F., Mathis, A., Mathis, M. W., & Patel, A.
- Affiliations
- University of Cape Town (ZA)
- University of Tsukuba (JP)
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2021-05-30
- Pages
- 13901-13908
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3136150335
- Cited by
- 66
- References
- 53
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 14 · 2023: 17 · 2022: 9 · 2021: 5
- FWCI
- 3.68
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- National Research Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- cheetah, 3D pose estimation, Animal locomotion, whole-body kinematics, markerless pose estimation, 2D keypoint detection, sparse bundle adjustment, extended Kalman filter, trajectory optimization, high-speed video, legged robot locomotion, biomechanics
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