Human action recognition by fuzzy hidden Markov model

2017 ◽  
Vol 7 (5) ◽  
pp. 538
Author(s):  
Jalal A. Nasiri ◽  
Nasrollah Moghadam Charkari ◽  
Kourosh Mozafari
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Action Recognition ◽  
Hidden Markov ◽  
Human Action Recognition ◽  
Human Action

scholarly journals Human action recognition based on mixed gaussian hidden markov model

2021 ◽  
Vol 336 ◽  
pp. 06004
Author(s):  
Jiawei Xu ◽  
Qian Luo
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Action Recognition ◽  
Hidden Markov ◽  
Human Action Recognition ◽  
Gaussian Mixture ◽  
Human Action ◽  
Model Based

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.

Human action recognition by fuzzy hidden Markov model

2017 ◽  
Vol 7 (5) ◽  
pp. 538 ◽  
Author(s):  
Jalal A. Nasiri ◽  
Nasrollah Moghadam Charkari ◽  
Kourosh Mozafari
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Action Recognition ◽  
Hidden Markov ◽  
Human Action Recognition ◽  
Human Action

Human Action Recognition with Expandable Graphical Models

2010 ◽  
pp. 187-212 ◽  
Author(s):  
Wanqing Li ◽  
Zhengyou Zhang ◽  
Zicheng Liu ◽  
Philip Ogunbona
Keyword(s):  
Markov Model ◽  
Action Recognition ◽  
Graphical Models ◽  
Graphical Model ◽  
Hidden Markov ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Actions ◽  
Weighted Directed Graph ◽  
Multiple Paths

This chapter first presents a brief review of the recent development in human action recognition. In particular, the principle and shortcomings of the conventional Hidden Markov Model (HMM) and its variants are discussed. We then introduce an expandable graphical model that represents the dynamics of human actions using a weighted directed graph, referred to as action graph. Unlike the conventional HMM, the action graph is shared by all actions to be recognized with each action being encoded in one or multiple paths and, thus, can be effectively and efficiently trained from a small number of samples. Furthermore, the action graph is expandable to incorporate new actions without being retrained and compromised. To verify the performance of the proposed expandable graphic model, a system that learns and recognizes human actions from sequences of silhouettes is developed and promising results are obtained.

Sign in / Sign up

Export Citation Format

Share Document