A Survey on Artificial Intelligence in Posture Recognition  

作　　者：Xiaoyan Jiang Zuojin Hu Shuihua Wang Yudong Zhang 

机构地区：[1]School of Mathematics and Information Science,Nanjing Normal University of Special Education,Nanjing,210038,China [2]School of Computing and Mathematical Sciences,University of Leicester,Leicester,LE17RH,UK

出　　处：《Computer Modeling in Engineering & Sciences》2023年第10期35-82,共48页工程与科学中的计算机建模（英文）

基　　金：supported by British Heart Foundation Accelerator Award,UK(AA/18/3/34220);Royal Society International Exchanges Cost Share Award,UK(RP202G0230);Hope Foundation for Cancer Research,UK(RM60G0680);Medical Research Council Confidence in Concept Award,UK(MC_PC_17171);Sino-UK Industrial Fund,UK(RP202G0289);Global Challenges Research Fund(GCRF),UK(P202PF11);LIAS Pioneering Partnerships award,UK(P202ED10);Data Science Enhancement Fund,UK(P202RE237);Fight for Sight,UK(24NN201);Sino-UK Education Fund,UK(OP202006).

摘　　要：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 vectormachine(SVM),Gaussian mixturemodel,dynamic time warping,hiddenMarkovmodel(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 posemachines,and high-resolution nets.The general process and datasets of posture recognition are analyzed and summarized,and several improved CNNmethods and threemain 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 3Dbenchmark data sets,data generation is a critical research direction.

关 键 词：Posture recognition artificial intelligence machine learning deep neural network deep learning transfer learning feature extraction CLASSIFICATION 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]