Joska, Daniel;
Clark, Liam;
Muramatsu, Naoya;
Jericevich, Ricardo;
Nicolls, Fred;
Mathis, Alexander;
Mathis, Mackenzie W;
(2021)
AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild.
In:
2021 IEEE International Conference on Robotics and Automation (ICRA).
(pp. pp. 13901-13908).
IEEE: Xi'an, China.
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Abstract
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 whole-body 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.
| Type: | Proceedings paper |
|---|---|
| Title: | AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild |
| Event: | IEEE International Conference on Robotics and Automation (ICRA) |
| Location: | PEOPLES R CHINA, Xian |
| Dates: | 30 May 2021 - 5 Jun 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICRA48506.2021.9561338 |
| Publisher version: | https://doi.org/10.1109/icra48506.2021.9561338 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Automation & Control Systems, Robotics, STATE ESTIMATION, MOTION CAPTURE, SYSTEMS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216271 |
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