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Event-Based Gesture Recognition With Dynamic Background Suppression Using Smartphone Computational Capabilities.

Jean-Matthieu Maro1, Sio-Hoi Ieng1,2, Ryad Benosman1,2,3,4

  • 1Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.

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Summary
This summary is machine-generated.

This study presents a novel framework for dynamic gesture recognition on mobile phones using event-based cameras. It enables real-time human-machine interaction for the visually impaired and older adults without needing powerful hardware.

Keywords:
background suppressiondynamic gesture recognitiondynamic vision sensor (DVS)event-basedgesture recognitionmobile deviceneuromorphicsmartphone

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Area of Science:

  • Computer Vision
  • Robotics
  • Human-Computer Interaction

Background:

  • Dynamic gesture recognition is crucial for intuitive human-machine interaction.
  • Existing methods often require significant computational resources or specialized hardware.
  • Event-based cameras offer high temporal resolution, suitable for real-time applications.

Purpose of the Study:

  • To introduce a real-time dynamic gesture recognition framework for mobile phones.
  • To develop a novel background suppression technique for event-based cameras.
  • To create a new publicly available event-based dataset for gesture recognition.

Main Methods:

  • A new framework utilizing time-surfaces for dynamic gesture recognition.
  • An event-based methodology for dynamic background removal.
  • Implementation on an Android smartphone leveraging its native computational power.

Main Results:

  • The first Android event-based framework for dynamic gesture recognition on smartphones without off-board processing.
  • Successful performance assessment in diverse indoor/outdoor and lighting conditions.
  • Comparable results to prior work without advanced classification or power-hungry hardware.

Conclusions:

  • The developed framework enables efficient, real-time dynamic gesture recognition on mobile devices.
  • The novel background suppression technique enhances recognition accuracy in complex environments.
  • The publicly released dataset and framework facilitate further research in accessible human-machine interaction.