Predictive Modeling of Equine Activity Budgets Using a 3D Skeleton Reconstructed from Surveillance Recordings

• URL: http://arxiv.org/abs/2306.05311v1
• Date: Thu, 8 Jun 2023 16:00:04 GMT
• Title: Predictive Modeling of Equine Activity Budgets Using a 3D Skeleton Reconstructed from Surveillance Recordings
• Authors: Ernest Pokropek, Sofia Broom\'e, Pia Haubro Andersen, Hedvig Kjellstr\"om
• Abstract summary: We present a pipeline to reconstruct the 3D pose of a horse from 4 simultaneous surveillance camera recordings. Our environment poses interesting challenges to tackle, such as limited field view of the cameras and a relatively closed and small environment.
• Score: 0.8602553195689513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present a pipeline to reconstruct the 3D pose of a horse from 4 simultaneous surveillance camera recordings. Our environment poses interesting challenges to tackle, such as limited field view of the cameras and a relatively closed and small environment. The pipeline consists of training a 2D markerless pose estimation model to work on every viewpoint, then applying it to the videos and performing triangulation. We present numerical evaluation of the results (error analysis), as well as show the utility of the achieved poses in downstream tasks of selected behavioral predictions. Our analysis of the predictive model for equine behavior showed a bias towards pain-induced horses, which aligns with our understanding of how behavior varies across painful and healthy subjects.

Related papers

• CLHOP: Combined Audio-Video Learning for Horse 3D Pose and Shape Estimation [8.722147241142736]
  We explore using audio to enhance 3D shape and motion recovery of horses from monocular video. This study is the first to investigate audio's role in 3D animal motion recovery.
  arXiv  Detail & Related papers  (2024-07-01T12:39:50Z)
• PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions [57.871692507044344]
  Pose estimation aims to accurately identify anatomical keypoints in humans and animals using monocular images. Current models are typically trained and tested on clean data, potentially overlooking the corruption during real-world deployment. We introduce PoseBench, a benchmark designed to evaluate the robustness of pose estimation models against real-world corruption.
  arXiv  Detail & Related papers  (2024-06-20T14:40:17Z)
• SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers [57.46911575980854]
  We introduce SkelFormer, a novel markerless motion capture pipeline for multi-view human pose and shape estimation. Our method first uses off-the-shelf 2D keypoint estimators, pre-trained on large-scale in-the-wild data, to obtain 3D joint positions. Next, we design a regression-based inverse-kinematic skeletal transformer that maps the joint positions to pose and shape representations from heavily noisy observations.
  arXiv  Detail & Related papers  (2024-04-19T04:51:18Z)
• Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and Synthetic Prior [0.7499722271664145]
  We propose an approach for estimating 2D mouse body pose from unlabelled images using a synthetically generated empirical pose prior. We adapt this method to the limb structure of the mouse and generate the empirical prior of 2D poses from a synthetic 3D mouse model. In experiments on a new mouse video dataset, we evaluate the performance of the approach by comparing pose predictions to a manually obtained ground truth.
  arXiv  Detail & Related papers  (2023-07-25T09:31:55Z)
• On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation [57.766049538913926]
  Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. Much of the recent attention has shifted towards semi and (or) weakly supervised learning. We propose to impose multi-view geometrical constraints by means of a differentiable triangulation and to use it as form of self-supervision during training when no labels are available.
  arXiv  Detail & Related papers  (2022-03-29T19:11:54Z)
• Evaluation of deep lift pose models for 3D rodent pose estimation based on geometrically triangulated data [1.84316002191515]
  Behavior is typically studied in terms of pose changes, which are ideally captured in three dimensions. This requires triangulation over a multi-camera system which view the animal from different angles. Here we propose the usage of lift-pose models that allow for robust 3D pose estimation of freely moving rodents from a single view camera view.
  arXiv  Detail & Related papers  (2021-06-24T13:08:33Z)
• Self-Attentive 3D Human Pose and Shape Estimation from Videos [82.63503361008607]
  We present a video-based learning algorithm for 3D human pose and shape estimation. We exploit temporal information in videos and propose a self-attention module. We evaluate our method on the 3DPW, MPI-INF-3DHP, and Human3.6M datasets.
  arXiv  Detail & Related papers  (2021-03-26T00:02:19Z)
• Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation [58.72192168935338]
  Generalizability of human pose estimation models developed using supervision on large-scale in-studio datasets remains questionable. We propose a novel kinematic-structure-preserved unsupervised 3D pose estimation framework, which is not restrained by any paired or unpaired weak supervisions. Our proposed model employs three consecutive differentiable transformations named as forward-kinematics, camera-projection and spatial-map transformation.
  arXiv  Detail & Related papers  (2020-06-24T23:56:33Z)
• Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis [72.34794624243281]
  We propose a self-supervised learning framework to disentangle variations from unlabeled video frames. Our differentiable formalization, bridging the representation gap between the 3D pose and spatial part maps, allows us to operate on videos with diverse camera movements.
  arXiv  Detail & Related papers  (2020-04-09T07:55:01Z)

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.