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A Survey on Artificial Intelligence in Posture Recognition.

Xiaoyan Jiang1,2, Zuojin Hu1, Shuihua Wang2

  • 1School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing, 210038, China.

Computer Modeling in Engineering & Sciences : CMES
|May 8, 2023
PubMed
Summary

This review explores recent advancements in posture recognition technology. Convolutional Neural Networks (CNNs) show great success, but further research in feature extraction and data generation is crucial for improved human pose estimation.

Keywords:
Posture recognitionartificial intelligenceclassificationdeep learningdeep neural networkfeature extractionmachine learningtransfer learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Technological advancements have significantly expanded the scope and applications of posture recognition.
  • Research in posture recognition has evolved with new methodologies and algorithms.

Purpose of the Study:

  • To introduce the latest methods in posture recognition.
  • To review recent techniques and algorithms, including CNNs and their enhancements.
  • To analyze datasets and compare different recognition approaches.

Main Methods:

  • Review of traditional methods: Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Support Vector Machine (SVM), Gaussian Mixture Model (GMM), Dynamic Time Warping (DTW), Hidden Markov Model (HMM).
  • Investigation of Convolutional Neural Network (CNN) advancements: Stacked Hourglass Networks, Multi-stage Pose Estimation Networks, Convolutional Pose Machines, High-Resolution Nets.
  • Exploration of advanced neural network applications: Transfer Learning, Ensemble Learning, Graph Neural Networks, Explainable Deep Neural Networks.

Main Results:

  • CNNs have demonstrated significant success and are favored by researchers in posture recognition.
  • Hidden Markov Models (HMM) and Support Vector Machines (SVM) remain widely used classification methods.
  • Lightweight networks are emerging as a notable area of research interest.

Conclusions:

  • While CNNs excel, further research is needed in feature extraction and information fusion for posture recognition.
  • The development of 3D benchmark datasets and data generation techniques is a critical future research direction.
  • Continued exploration of advanced neural network architectures is essential for advancing the field.