AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild

• URL: http://arxiv.org/abs/2103.13282v1
• Date: Wed, 24 Mar 2021 15:54:11 GMT
• Title: AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild
• Authors: Daniel Joska and Liam Clark and Naoya Muramatsu and Ricardo Jericevich and Fred Nicolls and Alexander Mathis and Mackenzie W. Mathis and Amir Patel
• Abstract summary: We present an extensive dataset of free-running cheetahs in the wild, called AcinoSet. The dataset contains 119,490 frames of multi-view synchronized high-speed video footage, camera calibration files and 7,588 human-annotated frames. The resulting 3D trajectories, human-checked 3D ground truth, and an interactive tool to inspect the data is also provided.
• Score: 51.35013619649463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals [7.039426581802364]
  SINETRA is a versatile simulator that generates synthetic tracking data for particles on a deformable background. This simulator produces annotated 2D and 3D videos that reflect the intricate movements seen in behaving animals like Hydra Vulgaris.
  arXiv  Detail & Related papers  (2024-11-14T14:12:16Z)
• Wild Motion Unleashed: Markerless 3D Kinematics and Force Estimation in Cheetahs [3.396214578939738]
  We use data obtained from cheetahs in the wild to present a trajectory optimisation approach for estimating the 3D kinematics and joint torques of subjects remotely. We are able to reconstruct the 3D kinematics with an average reprojection error of 17.69 pixels (62.94 $%$ PCK using the nose-to-eye(s) length segment as a threshold) While the joint torques cannot be directly validated against ground truth data, the estimated torques agree with previous studies of quadrupeds in controlled settings.
  arXiv  Detail & Related papers  (2023-12-10T13:14:58Z)
• 3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking [14.52333427647304]
  We present 3D-MuPPET, a framework to estimate and track 3D poses of up to 10 pigeons at interactive speed using multiple camera views. For identity matching, we first dynamically match 2D detections to global identities in the first frame, then use a 2D tracker to maintain IDs across views in subsequent frames. We show that 3D-MuPPET also works in outdoors without additional annotations from natural environments.
  arXiv  Detail & Related papers  (2023-08-29T14:02:27Z)
• PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking [90.29143475328506]
  We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. We animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos.
  arXiv  Detail & Related papers  (2023-07-27T17:58:11Z)
• Towards Single Camera Human 3D-Kinematics [15.559206592078425]
  We propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines.
  arXiv  Detail & Related papers  (2023-01-13T08:44:09Z)
• HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving [95.42203932627102]
  3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians. Our method efficiently makes use of these complementary signals, in a semi-supervised fashion and outperforms existing methods with a large margin. Specifically, we embed LiDAR points into pixel-aligned multi-modal features, which we pass through a sequence of Transformer refinement stages.
  arXiv  Detail & Related papers  (2022-12-15T11:15:14Z)
• FLAG3D: A 3D Fitness Activity Dataset with Language Instruction [89.60371681477791]
  We present FLAG3D, a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. We show that FLAG3D contributes great research value for various challenges, such as cross-domain human action recognition, dynamic human mesh recovery, and language-guided human action generation.
  arXiv  Detail & Related papers  (2022-12-09T02:33:33Z)
• MBW: Multi-view Bootstrapping in the Wild [30.038254895713276]
  Multi-camera systems that train fine-grained detectors have shown promise in detecting such errors. The approach is based on calibrated cameras and rigid geometry, making it expensive, difficult to manage, and impractical in real-world scenarios. In this paper, we address these bottlenecks by combining a non-rigid 3D neural prior with deep flow to obtain high-fidelity landmark estimates. We are able to produce 2D results comparable to state-of-the-art fully supervised methods, along with 3D reconstructions that are impossible with other existing approaches.
  arXiv  Detail & Related papers  (2022-10-04T16:27:54Z)
• Gait Recognition in the Wild with Dense 3D Representations and A Benchmark [86.68648536257588]
  Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. This paper aims to explore dense 3D representations for gait recognition in the wild. We build the first large-scale 3D representation-based gait recognition dataset, named Gait3D.
  arXiv  Detail & Related papers  (2022-04-06T03:54:06Z)
• Monocular Quasi-Dense 3D Object Tracking [99.51683944057191]
  A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform.
  arXiv  Detail & Related papers  (2021-03-12T15:30:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.