Open Access

REVIEW

A Survey on Artificial Intelligence in Posture Recognition

by Xiaoyan Jiang^1,2, Zuojin Hu¹, Shuihua Wang², Yudong Zhang^2,*

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:

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

Received 08 December 2022; Accepted 05 January 2023; Issue published 23 April 2023

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.

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Keywords

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