Human action recognition based on mixed gaussian hidden markov model
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...
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EDP Sciences
2021-01-01
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Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2021/05/matecconf_cscns20_06004.pdf |
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doaj-9302ce86f36e4e2984d5fd89c55f34212021-02-18T10:45:18ZengEDP SciencesMATEC Web of Conferences2261-236X2021-01-013360600410.1051/matecconf/202133606004matecconf_cscns20_06004Human action recognition based on mixed gaussian hidden markov modelXu Jiawei0Luo Qian1Beijing Information Science and Technology UniversityBeijing Information Science and Technology UniversityHuman 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.https://www.matec-conferences.org/articles/matecconf/pdf/2021/05/matecconf_cscns20_06004.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xu Jiawei Luo Qian |
spellingShingle |
Xu Jiawei Luo Qian Human action recognition based on mixed gaussian hidden markov model MATEC Web of Conferences |
author_facet |
Xu Jiawei Luo Qian |
author_sort |
Xu Jiawei |
title |
Human action recognition based on mixed gaussian hidden markov model |
title_short |
Human action recognition based on mixed gaussian hidden markov model |
title_full |
Human action recognition based on mixed gaussian hidden markov model |
title_fullStr |
Human action recognition based on mixed gaussian hidden markov model |
title_full_unstemmed |
Human action recognition based on mixed gaussian hidden markov model |
title_sort |
human action recognition based on mixed gaussian hidden markov model |
publisher |
EDP Sciences |
series |
MATEC Web of Conferences |
issn |
2261-236X |
publishDate |
2021-01-01 |
description |
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. |
url |
https://www.matec-conferences.org/articles/matecconf/pdf/2021/05/matecconf_cscns20_06004.pdf |
work_keys_str_mv |
AT xujiawei humanactionrecognitionbasedonmixedgaussianhiddenmarkovmodel AT luoqian humanactionrecognitionbasedonmixedgaussianhiddenmarkovmodel |
_version_ |
1724263114890805248 |