https://github.com/exponentialr/qub-hri

Preprocessing Repository of QUB-Perception of Human Enagagement in Assembly Operations Dataset
https://github.com/exponentialr/qub-hri

computer-vision dataset preprocessing synchronization

Last synced: 6 months ago
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Preprocessing Repository of QUB-Perception of Human Enagagement in Assembly Operations Dataset

• Host: GitHub
• URL: https://github.com/exponentialr/qub-hri
• Owner: exponentialR
• Created: 2023-09-04T02:42:27.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2025-09-23T12:09:09.000Z (10 months ago)
• Last Synced: 2025-09-23T14:23:02.497Z (10 months ago)
• Topics: computer-vision, dataset, preprocessing, synchronization
• Language: Python
• Homepage:
• Size: 91 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md
  • Citation: CITATION.cff

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README

          
# Repository of Preprocessing of QUB-Perception of Human Engagement in assembly Operations Dataset (QUB-PHEO V1.0) 

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13956098.svg)](https://doi.org/10.5281/zenodo.13956098)

![GitHub](https://img.shields.io/github/license/exponentialR/QUB-HRI)

## Introduction

![QUB-PHEO-Overview](media/qub-pheo.gif)

## Description

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 paves the way for proactive and adaptive robotic collaboration in real-world settings.

## Dataset

## Preprocessing

## Eula and License

To get access to the dataset, please download and fill out the [End User License Agreement](https://github.com/exponentialR/QUB-HRI/license/EULA.md) and send it to [Samuel Adebayo](mailto:samueladebayo@ieee.org)

In using this dataset, you agree to the terms of the license described in the LICENSE file included in this repository.

## What is in the Dataset

- The dataset contains the following:

  - `Annotations` folder: This folder contains the annotations for the dataset. The annotations are in the form of hdf5 files.

  - `Videos` folder: This folder contains the videos for the dataset. The videos are in the form of mp4 files.

  - `README.md` file: This file contains the description of the dataset.

  - `LICENSE` file: This file contains the license for the dataset.

  - `EULA` file: This file contains the End User License Agreement for the dataset.

## Citation

If you use this code for your research, please cite our paper.

```bibtex

@misc{adebayo_exponentialrqub-hri_2024,

	title = {{exponentialR}/{QUB}-{HRI}: v1.1},

	shorttitle = {{exponentialR}/{QUB}-{HRI}},

	url = {https://zenodo.org/records/13956098},

	abstract = {Preprocessing Repository of QUB-Perception of Human Enagagement in Assembly Operations Dataset},

	urldate = {2024-10-19},

	publisher = {Zenodo},

	author = {Adebayo, Samuel},

	month = oct,

	year = {2024},

	doi = {10.5281/zenodo.13956098},

}