ArticleSensors (Basel, Switzerland)2019
3D Pose Detection of Closely Interactive Humans Using Multi-View Cameras.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 22 citations in OpenAlex.
- The Poses for Equine Research Dataset (PFERD).Scientific data · 2024Article
- Multi-Camera-Based Human Activity Recognition for Human-Robot Collaboration in Construction.Sensors (Basel, Switzerland) · 2023Article
- Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection.Sensors (Basel, Switzerland) · 2021Article
- Rock Particle Motion Information Detection Based on Video Instance Segmentation.Sensors (Basel, Switzerland) · 2021Article
- Robust Extrinsic Calibration of Multiple RGB-D Cameras with Body Tracking and Feature Matching.Sensors (Basel, Switzerland) · 2021Article
- Human 3D Pose Estimation with a Tilting Camera for Social Mobile Robot Interaction.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a method to automatically detect 3D poses of closely interactive humans from sparse multi-view images at one time instance. It is a challenging problem due to the strong partial occlusion and truncation between humans and no tracking process to provide priori poses information. To solve this problem, we first obtain 2D joints in every image using OpenPose and human semantic segmentation results from Mask R-CNN. With the 3D joints triangulated from multi-view 2D joints, a two-stage assembling method is proposed to select the correct 3D pose from thousands of pose seeds combined by joint semantic meanings. We further present a novel approach to minimize the interpenetration between human shapes with close interactions. Finally, we test our method on multi-view human-human interaction (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.