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A PointNet-Based Solution for 3D Hand Gesture Recognition.

Radu Mirsu1, Georgiana Simion1, Catalin Daniel Caleanu1

  • 1Applied Electronics Department, Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University Timișoara, Timișoara 300223, Romania.

Sensors (Basel, Switzerland)
|June 11, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a deep neural network using PointNet for hand gesture recognition with time-of-flight (ToF) sensor data. The 3D point cloud method significantly outperforms 2D image approaches for accurate gesture recognition.

Keywords:
PointNethand gesture recognitiontime of flight sensors

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Gesture recognition is vital for applications in robotics, gaming, and medicine.
  • Three-dimensional (3D) imaging techniques overcome limitations of 2D methods.
  • Deep neural networks increasingly outperform traditional methods in computer vision.

Purpose of the Study:

  • To develop a deep neural network solution for hand gesture recognition using 3D depth data.
  • To evaluate the effectiveness of the PointNet architecture with time-of-flight (ToF) sensor data.
  • To compare the performance of 3D point cloud data against 2D image data for gesture recognition.

Main Methods:

  • A custom hand gesture dataset was created using a ToF sensor.
  • A multistage hand segmentation process involving filtering and clustering was developed.
  • The PointNet deep neural network architecture was employed for gesture recognition.

Main Results:

  • The proposed 3D method using PointNet achieved higher accuracy than 2D methods.
  • Performance comparison included 3D point cloud and 2D image datasets from the same data stream.
  • The 3D approach demonstrated superior accuracy even against 2D deep neural network methods.

Conclusions:

  • 3D gesture recognition using PointNet with ToF data offers superior accuracy.
  • The developed hand segmentation technique is effective for processing 3D depth data.
  • This research validates the advantage of 3D data over 2D data in hand gesture recognition tasks.