Open Access iconOpen Access

REVIEW

crossmark

A Survey on Artificial Intelligence in Posture Recognition

Xiaoyan Jiang1,2, Zuojin Hu1, Shuihua Wang2, Yudong Zhang2,*

1 Nanjing Normal University of Special Education, Nanjing, 210038, China
2 School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK

* Corresponding Author: Yudong Zhang. Email: email

Computer Modeling in Engineering & Sciences 2023, 137(1), 35-82. https://doi.org/10.32604/cmes.2023.027676

Abstract

Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

Keywords


Cite This Article

APA Style
Jiang, X., Hu, Z., Wang, S., Zhang, Y. (2023). A survey on artificial intelligence in posture recognition. Computer Modeling in Engineering & Sciences, 137(1), 35-82. https://doi.org/10.32604/cmes.2023.027676
Vancouver Style
Jiang X, Hu Z, Wang S, Zhang Y. A survey on artificial intelligence in posture recognition. Comput Model Eng Sci. 2023;137(1):35-82 https://doi.org/10.32604/cmes.2023.027676
IEEE Style
X. Jiang, Z. Hu, S. Wang, and Y. Zhang "A Survey on Artificial Intelligence in Posture Recognition," Comput. Model. Eng. Sci., vol. 137, no. 1, pp. 35-82. 2023. https://doi.org/10.32604/cmes.2023.027676



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 3272

    View

  • 1119

    Download

  • 1

    Like

Share Link