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Dual Leap Motion Controller 2: A Robust Dataset for Multi-view Hand Pose Recognition.

Manuel Gil-Martín1, Marco Raoul Marini2, Rubén San-Segundo3

  • 1Grupo de Tecnología del Habla y Aprendizaje Automático (T.H.A.U. Group), Department of Electrical Engineering, Information Processing and Telecommunications Center, E.T.S.I. de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain. manuel.gilmartin@upm.es.

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Summary
This summary is machine-generated.

Researchers created the Multi-view Leap2 Hand Pose Dataset (ML2HP Dataset) for advanced hand pose recognition. This dataset uses multiple camera views to capture diverse hand movements and properties for AI applications.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Accurate hand pose recognition is crucial for natural human-computer interaction (HCI).
  • Existing datasets often suffer from limited viewpoints and occlusion, hindering real-world application development.
  • The Leap Motion Controller 2 offers precise hand tracking capabilities.

Purpose of the Study:

  • To introduce the Multi-view Leap2 Hand Pose Dataset (ML2HP Dataset), a novel resource for hand pose recognition.
  • To provide a comprehensive dataset addressing limitations of previous hand pose datasets, particularly regarding occlusion and viewpoint diversity.
  • To facilitate the development of advanced AI-driven HCI systems.

Main Methods:

  • The ML2HP Dataset was captured using a multi-view recording setup with two Leap Motion Controller 2 devices.
  • The dataset includes 714,000 instances from 21 subjects, featuring 17 distinct hand poses.
  • Precise hand properties, including landmark coordinates, velocities, orientations, and finger widths, were automatically extracted.

Main Results:

  • The dataset offers a balanced representation of subjects, hand poses, and hand laterality (left/right).
  • The multi-view approach effectively mitigates hand occlusion, enabling continuous tracking and pose estimation.
  • The dataset contains 247 associated hand properties per instance, providing rich information for model training.

Conclusions:

  • The ML2HP Dataset is a valuable resource for advancing multimodal hand pose recognition research.
  • This dataset will accelerate the development of more robust and accurate AI for human-computer interaction.
  • The comprehensive nature of the dataset supports the creation of sophisticated HCI applications.