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An Adaptive Human Posture Detection Algorithm Based on Generative Adversarial Network.

Zhiming Xu1, Wenzheng Qu2, Hanhua Cao1

  • 1Guangzhou Xinhua University, Guangzhou 510000, Guangdong, China.

Computational Intelligence and Neuroscience
|April 11, 2022
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Summary
This summary is machine-generated.

This study introduces an adaptive generative adversarial network (GAN) to enhance human posture detection accuracy. The improved algorithm achieves high positioning accuracy, outperforming existing methods in joint detection.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Biomechanical Engineering

Background:

  • Deep learning and machine vision have advanced human posture equipment technology.
  • Current advanced models struggle with accurate prediction of all human body joints.

Purpose of the Study:

  • To propose an adaptive generative adversarial network (GAN) to improve human posture detection algorithms.
  • To enhance the accuracy and reliability of human joint detection in various datasets.

Main Methods:

  • Utilized OpenPose for keypoint detection and connection.
  • Implemented a GAN system model generating heat maps for posture analysis.
  • Integrated a confidence evaluation mechanism with normalization technologies during training.

Main Results:

  • Achieved approximately 95.37% positioning accuracy in human joint detection.
  • Demonstrated superior performance over several other human posture detection algorithms.
  • Exhibited the best simultaneous runtime on the LSP dataset.

Conclusions:

  • The proposed adaptive GAN significantly improves human posture detection accuracy.
  • The algorithm accurately locates entire body joints, showing robust performance across MPII, LSP, and FLIC datasets.
  • This method offers a promising advancement in human posture analysis technology.