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Optimization Method of Human Posture Recognition Based on Kinect V2 Sensor.

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  • 1School of Information Engineering, Xi'an University, Xi'an 710065, China.

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Summary
This summary is machine-generated.

This study introduces a novel human action recognition method using Kinect, OpenPose, Levenberg-Marquardt (LM), and Dynamic Time Warping (DTW) algorithms. The approach achieves 95.94% accuracy, enhancing human-computer interaction and robotics.

Keywords:
bone point optimizationhuman action recognitionhuman–computer interaction

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Area of Science:

  • Computer Vision
  • Robotics
  • Human-Computer Interaction

Background:

  • Human action recognition is vital for intelligent systems.
  • Existing methods face challenges with data complexity and real-time processing.
  • Kinect sensor data can be prone to jitter and self-occlusion.

Purpose of the Study:

  • To enhance the complexity and real-time performance of human action recognition.
  • To develop a robust method for recognizing human actions in various scenarios.
  • To improve the stability and accuracy of skeletal data acquisition.

Main Methods:

  • Combined Kinect V2 sensor with OpenPose, Levenberg-Marquardt (LM), and Dynamic Time Warping (DTW) algorithms.
  • Extracted 2D skeletal data using OpenPose, mapped it to depth images for 3D skeletal data.
  • Fused Kinect and OpenPose 3D skeletal data using LM, then recognized actions with DTW.

Main Results:

  • Achieved an average action recognition rate of 95.94% across diverse scenes and actions.
  • Successfully addressed issues like jitter and self-occlusion in Kinect skeletal data.
  • Demonstrated stability and accuracy in complex human movement recognition.

Conclusions:

  • The proposed method offers a stable and accurate solution for human action recognition.
  • It significantly improves the quality of skeletal data compared to using Kinect alone.
  • The approach is highly suitable for real-world applications in robot interaction systems.