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Fusion of Video and Inertial Sensing for Deep Learning-Based Human Action Recognition.

Haoran Wei1, Roozbeh Jafari2, Nasser Kehtarnavaz3

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Summary
This summary is machine-generated.

Combining video and wearable inertial sensor data significantly improves human action recognition. Fusion methods, particularly decision-level fusion, achieved the highest accuracy at 95.6%.

Keywords:
decision-level and feature-level fusion for action recognitiondeep learning-based action recognitionfusion of video and inertial sensing for action recognition

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Human action recognition is crucial for various applications.
  • Individual sensor modalities (video or inertial) have limitations in accuracy.
  • Sensor fusion offers a promising approach to enhance recognition robustness.

Purpose of the Study:

  • To investigate the effectiveness of fusing video images and inertial signals for human action recognition.
  • To compare decision-level fusion and feature-level fusion strategies.
  • To achieve more robust and accurate human action recognition.

Main Methods:

  • Simultaneous capture of video images and inertial signals using a camera and wearable sensor.
  • Processing video data into 3D images and inertial data into 2D images.
  • Utilizing 3D and 2D convolutional neural networks (CNNs) with fusion techniques.
  • Experimentation on the UTD-MHAD dataset with 27 distinct actions.

Main Results:

  • Both decision-level and feature-level fusion approaches outperformed individual sensor modalities.
  • Decision-level fusion yielded a superior recognition accuracy of 95.6%.
  • The fusion framework demonstrated enhanced robustness in human action recognition.

Conclusions:

  • Fusing video and inertial sensor data is highly effective for human action recognition.
  • Decision-level fusion provides the most accurate results among the investigated methods.
  • This approach offers a significant advancement for robust human action recognition systems.