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Feature Fusion of Deep Spatial Features and Handcrafted Spatiotemporal Features for Human Action Recognition.

Md Azher Uddin1, Young-Koo Lee2

  • 1Department of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin 17104, Korea. azher006@yahoo.com.

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|April 17, 2019
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Summary

This study introduces a new human action recognition method combining deep spatial features from Inception-Resnet-v2 and novel handcrafted spatiotemporal features (WVLGTP). The approach achieves superior accuracy compared to existing methods.

Keywords:
Inception-Resnet-v2Weber’s law based volume local gradient ternary patterndeep spatial featuresspatiotemporal features

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human action recognition is crucial for AI applications.
  • Existing methods struggle with spatial-temporal information extraction.
  • Effective video description requires robust feature aggregation.

Purpose of the Study:

  • To propose a novel human action recognition approach.
  • To integrate deep spatial and handcrafted spatiotemporal features.
  • To improve the accuracy of action recognition systems.

Main Methods:

  • Extracted deep spatial features using Inception-Resnet-v2.
  • Developed a novel Weber's Law based Volume Local Gradient Ternary Pattern (WVLGTP) for spatiotemporal features.
  • Concatenated features and classified using Support Vector Machine.

Main Results:

  • The proposed method demonstrated superior performance.
  • Achieved higher accuracy than current state-of-the-art approaches.
  • WVLGTP effectively handles noisy data and captures shape information.

Conclusions:

  • The hybrid feature approach significantly enhances action recognition.
  • The novel WVLGTP descriptor offers robust spatiotemporal feature extraction.
  • This method provides a promising direction for advanced human action recognition.