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Finger Gesture Spotting from Long Sequences Based on Multi-Stream Recurrent Neural Networks.

Gibran Benitez-Garcia1, Muhammad Haris1, Yoshiyuki Tsuda2

  • 1Toyota Technological Institute, Nagoya 468-8511, Japan.

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Summary
This summary is machine-generated.

This study introduces a new recurrent neural network for online finger gesture spotting in autonomous cars. The method improves gesture recognition accuracy by analyzing hand and location features, outperforming existing techniques.

Keywords:
automotive user interfacesgesture spottinghuman–computer interactionin-vehicle sensorsrecurrent neural networks

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Gesture spotting is crucial for touchless in-car interfaces.
  • Automated methods must detect gestures, differentiate them from natural hand movements, and operate in real-time.
  • Existing methods struggle with accuracy and online processing.

Purpose of the Study:

  • To develop an effective online finger gesture spotting method for autonomous vehicles.
  • To improve the accuracy and efficiency of gesture recognition in complex in-car environments.
  • To address the challenge of distinguishing target gestures from natural hand movements.

Main Methods:

  • A novel multi-stream recurrent neural network (RNN) architecture was proposed.
  • The network merges hand and hand-location features to enhance gesture discrimination.
  • The model was trained and validated on a custom finger gesture dataset captured in an autonomous car using a depth sensor.

Main Results:

  • The proposed gesture spotting approach achieved significant improvements in recall (10%) and precision (15%) compared to state-of-the-art methods.
  • The multi-stream RNN effectively differentiates target gestures from natural hand movements by considering spatial location.
  • Integration with a 3D Convolutional Neural Network classifier further boosted overall gesture recognition performance.

Conclusions:

  • The developed multi-stream RNN is a highly effective method for online finger gesture spotting in autonomous driving scenarios.
  • The approach offers superior performance in distinguishing gestures from non-gesture hand movements, enhancing user interface reliability.
  • This work advances the field of human-computer interaction for safer and more intuitive in-car control systems.