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
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
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.