QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly

Samuel Adebayo, Seán McLoone, Joost C. Dessing
9/23/2024
cs.CVcs.HCeess.IV

Official code: exponentialR/QUB-PHEO (5 stars, last commit 2026-04-15).

Abstract

QUB-PHEO introduces a visual-based, dyadic dataset with the potential of advancing human-robot interaction (HRI) research in assembly operations and intention inference. This dataset captures rich multimodal interactions between two participants, one acting as a 'robot surrogate,' across a variety of assembly tasks that are further broken down into 36 distinct subtasks. With rich visual annotations, such as facial landmarks, gaze, hand movements, object localization, and more for 70 participants, QUB-PHEO offers two versions: full video data for 50 participants and visual cues for all 70. Designed to improve machine learning models for HRI, QUB-PHEO enables deeper analysis of subtle interaction cues and intentions, promising contributions to the field. The dataset will be available at https://github.com/exponentialR/QUB-PHEO subject to an End-User License Agreement (EULA).

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Code Implementations(1)

exponentialR/QUB-PHEOOfficial100%

Repository for the QUB-Perception of Human Engagement in Assembly operation dataset

50PythonOct 19, 20246 months ago
datasetintentionintention-inferencequb-pheo

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Cite this paper

@article{adebayo2024qubpheo,
  title  = {QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly},
  author = {Samuel Adebayo and Seán McLoone and Joost C. Dessing},
  year   = {2024},
  eprint = {2409.15560},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.15560v1}
}

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