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

arXiv 2409.15560 ↗First posted 23 Sep 2024cs.CV cs.HC eess.IVcited by 0

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Abstract (arXiv)

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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References · 9

  1. 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,556
  2. 2020MediaPipe Hands: On-device Real-time Hand Trackingcited by 161
  3. 2020Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Datasetcited by 31
  4. 2023Nonverbal Cues in Human-Robot Interaction: A Communication Studies Perspectivecited by 16
  5. 2023Developing Social Robots with Empathetic Non-Verbal Cues Using Large Language Modelscited by 12
  6. 2020A Study on the Challenges of Using Robotics Simulators for Testingcited by 10
  7. 2020Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Datasetcited by 5
  8. 2020Presentation and Analysis of a Multimodal Dataset for Grounded Language Learningcited by 2
  9. 2021Improving Intention Detection in Single-Trial Classification through Fusion of EEG and Eye-tracker Datacited by 2

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