Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in README
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.2%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: exponentialR
  • Language: Python
  • Default Branch: master
  • Size: 60.9 MB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Citation

readme.md

Repository for the QUB-Perception of Human Engagement in assembly Operation Dataset

About QUB-PHEO

One of the core stages of efficient human-robot collaboration (HRC) is human-intention inference, enabling robots to anticipate and respond to human actions seamlessly. Existing approaches often rely on rule-based models or handcrafted heuristics, which lack adaptability to dynamic environments. In contrast, learning-based approaches leverage data-driven models to infer human intent, but their effectiveness depends on the availability of high-quality, multi-view datasets that capture rich spatial-temporal cues. To address this, we introduce QUB-PHEO, a novel visual-based dyadic multi-view dataset designed to enhance intention inference in HRC. The dataset consists of synchronized multi-view recordings of 70 participants performing 36 distinct assembly subtasks, providing fine-grained labels for action recognition, gaze estimation, and object tracking. By enabling deep learning models to learn intent prediction from diverse viewpoints, QUB-PHEO is a step towards proactive and adaptive robotic collaboration in real-world settings.

QUB-PHEO-Overview

Thank you for your interest in our dataset.

If you are looking for our QUB-PHEO dataset preprocessing pipeline, please refer to
this repository.

To request access to the dataset, please fill out this form and send to s.mcloone@qub.ac.uk for approval. Please specify the intended use of the dataset and the name of the research group or institution.

We are currently working on the dataset documentation and will be available soon.

If you find this dataset useful, please cite the following paper:

@ARTICLE{qub_pheo_2024, author={Adebayo, Samuel and McLoone, Seán and Dessing, Joost C.}, journal={IEEE Access}, title={QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly}, year={2024}, volume={12}, number={}, pages={157050-157066}, doi={10.1109/ACCESS.2024.3485162}}

Owner

  • Name: Samuel Adebayo
  • Login: exponentialR
  • Kind: user
  • Location: Belfast

PhD Student @QUBelfast Most of my repositories are private

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this dataset in your work, please cite it using the following information:"
title: "QUB – Perception of Human Engagement in Assembly Operation (PHEO) Dataset"
authors:
  - family-names: Adebayo
    given-names: Samuel
    orcid: https://orcid.org/0000-0002-8592-1196
date-released: 2024-10-19
version: "1.0.0"
doi: 10.5281/zenodo.13956074
repository-code: https://github.com/exponentialR/QUB-PHEO
license: MIT

GitHub Events

Total
  • Release event: 1
  • Watch event: 2
  • Push event: 31
  • Create event: 3
Last Year
  • Release event: 1
  • Watch event: 2
  • Push event: 31
  • Create event: 3