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  3. Human Behavior Recognition Via Hierarchical Patches Descriptor And Approximate Locality-constrained Linear Coding.
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  3. Human Behavior Recognition Via Hierarchical Patches Descriptor And Approximate Locality-constrained Linear Coding.

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Human Behavior Recognition via Hierarchical Patches Descriptor and Approximate Locality-Constrained Linear Coding.

Lina Liu1,2, Kevin I-Kai Wang2, Biao Tian3

  • 1College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China.

Sensors (Basel, Switzerland)
|June 10, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel human behavior recognition method using Hierarchical Patches Descriptor (HPD) and Approximate Locality-Constrained Linear Coding (ALLC). The approach achieves high accuracy in recognizing human actions across various datasets.

Keywords:
approximate locality-constrained linear coding algorithmenergy image specieshierarchical patches descriptorhuman behavior recognition

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human behavior recognition is crucial for intelligent surveillance and human-machine interaction.
  • Existing methods often face challenges in efficiency and accuracy.

Purpose of the Study:

  • To propose an efficient and accurate human behavior recognition method.
  • To combine detailed local feature description with fast coding techniques.

Main Methods:

  • Calculated energy image species for global human behavior description.
  • Constructed Hierarchical Patches Descriptor (HPD) using spatial pyramid matching for detailed description.
  • Employed Approximate Locality-Constrained Linear Coding (ALLC) for efficient patch encoding.

Main Results:

  • Achieved high recognition accuracy on Weizmann and DHA datasets.
  • Demonstrated superior performance with specific energy image species combined with HPD and ALLC.
  • Specific accuracies: 100% (MHI), 98.77% (MEI), 93.28% (AMEI), 94.68% (EMEI), 95.62% (MEnI).

Conclusions:

  • The proposed HPD and ALLC method offers a computationally efficient and accurate solution for human behavior recognition.
  • The combination of global and detailed feature descriptions enhances recognition performance.
  • The method shows significant potential for applications in intelligent surveillance and human-computer interaction.