QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909864287010816 |
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| author | Adebayo, Samuel McLoone, Seán Dessing, Joost C. |
| author_facet | Adebayo, Samuel McLoone, Seán Dessing, Joost C. |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15560 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly Adebayo, Samuel McLoone, Seán Dessing, Joost C. Computer Vision and Pattern Recognition Human-Computer Interaction Image and Video Processing Signal Processing 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). |
| title | QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2409.15560 |