Human action recognition using machine learning
The accurate detection of human pose is useful in various areas such as surveillance, clothes parsing and game modelling. While many existing models attempt to breach the gap, they suffer from problems including learning uninformative latent variables and being memory- intensive. In this work, we...
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Nanyang Technological University
2024
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sg-ntu-dr.10356-1750482024-04-19T15:41:46Z Human action recognition using machine learning Cao, QingTian Lin Weisi School of Computer Science and Engineering WSLin@ntu.edu.sg Computer and Information Science The accurate detection of human pose is useful in various areas such as surveillance, clothes parsing and game modelling. While many existing models attempt to breach the gap, they suffer from problems including learning uninformative latent variables and being memory- intensive. In this work, we propose FFPoser, a diffusion-based model that leverages the use of Fast-Fourier Transformation and a complex-valued neural network to resolve the above issues. It is able to generate fairly realistic poses and produce accurate occlusion recovery results. Additionally, it is easy to train while still be able to learn the important information of the source data. Bachelor's degree 2024-04-19T01:28:19Z 2024-04-19T01:28:19Z 2024 Final Year Project (FYP) Cao, Q. (2024). Human action recognition using machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175048 https://hdl.handle.net/10356/175048 en SCSE23-0613 application/pdf Nanyang Technological University |
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Computer and Information Science Cao, QingTian Human action recognition using machine learning |
| description |
The accurate detection of human pose is useful in various areas such as surveillance, clothes
parsing and game modelling. While many existing models attempt to breach the gap, they
suffer from problems including learning uninformative latent variables and being memory-
intensive. In this work, we propose FFPoser, a diffusion-based model that leverages the use
of Fast-Fourier Transformation and a complex-valued neural network to resolve the above
issues. It is able to generate fairly realistic poses and produce accurate occlusion recovery
results. Additionally, it is easy to train while still be able to learn the important information
of the source data. |
| author2 |
Lin Weisi |
| author_facet |
Lin Weisi Cao, QingTian |
| format |
Final Year Project |
| author |
Cao, QingTian |
| author_sort |
Cao, QingTian |
| title |
Human action recognition using machine learning |
| title_short |
Human action recognition using machine learning |
| title_full |
Human action recognition using machine learning |
| title_fullStr |
Human action recognition using machine learning |
| title_full_unstemmed |
Human action recognition using machine learning |
| title_sort |
human action recognition using machine learning |
| publisher |
Nanyang Technological University |
| publishDate |
2024 |
| url |
https://hdl.handle.net/10356/175048 |
| _version_ |
1814047151958786048 |