Human action recognition based on mixed gaussian hidden markov model

Jiawei Xu,Qian Luo,I Barukčić

doi:10.1051/matecconf/202133606004

Abstract

Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field. This paper uses a hidden Markov model based on mixed Gaussian to solve the problem of human action recognition. The model treats the observed human actions as samples which conform to the Gaussian mixture model, and each Gaussian mixture model is determined by a state variable. The training of the model is the process that obtain the model parameters through the expectation maximization algorithm. The simulation results show that the Hidden Markov Model based on the mixed Gaussian distribution can perform well in human action recognition.

Highlights

Due to the development of sensors, the data used for human action recognition has become more abundant [1]
Many methods have tried to solve the problem of human action recognition, such as random forest [1], variants of random forest [2], graph convolutional neural networks [3], Deep Progressive Reinforcement Learning [4], Directed Graph Neural Networks [5] and so on
Hidden Markov model establishes the joint probability distribution of hidden variables and observation variables and introduces mixed Gaussian distribution to approximate the real distribution of human movements, which can express the distribution of the data themselves better

Summary

Introduction

Due to the development of sensors, the data used for human action recognition has become more abundant [1]. Many methods have tried to solve the problem of human action recognition, such as random forest [1], variants of random forest [2], graph convolutional neural networks [3], Deep Progressive Reinforcement Learning [4], Directed Graph Neural Networks [5] and so on. Hidden Markov model establishes the joint probability distribution of hidden variables and observation variables and introduces mixed Gaussian distribution to approximate the real distribution of human movements, which can express the distribution of the data themselves better. It can perform well in human action recognition

Mixed gaussian hidden markov model

Expectation maximization algorithm

The simulation of algorithm

Method

Conclusion