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Updated: Aug 7, 2025

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Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique.

Najeeb Ur Rehman Malik1, Usman Ullah Sheikh1, Syed Abdul Rahman Abu-Bakar1

  • 1Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary

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This summary is machine-generated.

This study introduces a novel human action recognition (HAR) system using 2D skeleton features and an extraneous frame scrapping technique. The proposed OpenPose-FineKNN method achieves high accuracy, outperforming complex deep learning models for real-time applications.

Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Human Action Recognition (HAR) is a key research area in computer vision.
  • Existing HAR algorithms like 3D CNNs and CNN-LSTM are computationally intensive due to complex models and extensive weight adjustments.
  • These complex models necessitate high-end hardware for real-time performance.

Purpose of the Study:

  • To propose a novel, computationally efficient HAR system.
  • To overcome the dimensionality challenges associated with complex HAR models.
  • To develop a real-time HAR solution using simplified features and classification.

Main Methods:

  • An extraneous frame scrapping technique was developed.
  • 2D skeleton features were extracted using the OpenPose technique.
  • A Fine-KNN classifier was employed for action recognition.
Keywords:
EFSFineKNNHARMLOpenPoseskeleton

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  • The proposed method was evaluated on the Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset.

    • Main Results:

    • The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved 89.75% accuracy on the MCAD dataset.
    • The system attained 90.97% accuracy on the IXMAS dataset.
    • The proposed method demonstrated superior performance compared to existing state-of-the-art techniques like CNN-LSTM.

    Conclusions:

    • The proposed extraneous frame scrapping technique combined with 2D skeleton features and Fine-KNN offers an effective solution for HAR.
    • This approach successfully addresses the complexity and computational demands of traditional HAR algorithms.
    • The method shows significant potential for efficient and accurate real-time human action recognition.