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A Novel Parameter Initialization Technique Using RBM-NN for Human Action Recognition.

Deepika Roselind Johnson1, V Rhymend Uthariaraj2

  • 1DCSE, CEG-Anna University, Guindy, Chennai, India.

Computational Intelligence and Neuroscience
|September 23, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces a new deep learning method for human action recognition using Maxout activation and RBM-NN to improve accuracy. The novel technique enhances spatial-temporal feature extraction and classification for better video analysis.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Human action recognition is crucial in computer vision but faces accuracy and flexibility challenges.
  • Traditional methods struggle with complex actions and large datasets.
  • Deep learning offers self-improving models to overcome limitations like overfitting.

Purpose of the Study:

  • To propose a novel parameter initialization technique using the Maxout activation function for enhanced human action recognition.
  • To improve the accuracy and flexibility of human action recognition models.
  • To address limitations in existing state-of-the-art learning models.

Main Methods:

  • Human action detection and tracking for spatial-temporal feature extraction from video datasets.

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  • Training extracted feature descriptors using Restricted Boltzmann Machine-Neural Network (RBM-NN).
  • Encoding local features into global features via RBM-NN with integrated forward and backward propagation, followed by Support Vector Machine (SVM) classification.

    • Main Results:

    • The proposed method demonstrated improved recognition rates on benchmark datasets.
    • Experimental analysis showed superior performance compared to other state-of-the-art learning models.
    • The Maxout activation function and RBM-NN integration effectively enhanced feature learning and classification.

    Conclusions:

    • The novel parameter initialization technique significantly boosts human action recognition accuracy.
    • The integrated approach of RBM-NN and SVM provides a robust solution for complex action recognition tasks.
    • This research contributes a more accurate and flexible deep learning model for video-based human action analysis.