Human pose estimation using artificial intelligence

Over the past 10 years, human pose estimation (HPE) using artificial intelligence (AI) has gained more and more attention and been used in a range of applications, like human-computer interaction, motion analysis, healthcare, and security. The optimal goal of HPE is to use input data, such as pictur...

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Main Author: Zheng, Zhoudong
Other Authors: Yap Kim Hui
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176189
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1761892024-05-17T15:43:46Z Human pose estimation using artificial intelligence Zheng, Zhoudong Yap Kim Hui School of Electrical and Electronic Engineering EKHYap@ntu.edu.sg Engineering Human pose estimation Over the past 10 years, human pose estimation (HPE) using artificial intelligence (AI) has gained more and more attention and been used in a range of applications, like human-computer interaction, motion analysis, healthcare, and security. The optimal goal of HPE is to use input data, such as pictures and movies, to identify the various body parts and create a representation of the human body, such as skeleton and mesh. By leveraging and comparing different state-of-the-art (SOTA) deep learning models, such as convolutional neural networks (CNNs) and transformer-based structures, it is found out that insufficient learning of spatial-temporal correlation results from the prior approaches’ inability to effectively represent each joint’s solid inter-frame relationship. Among the models, MixSTE and its baseline model, VideoPose3D, have innovative idea to solve the problems. Therefore, the purpose of this report is to improve the accuracy as well as robustness of the 3D HPE by improving the current models. The present model is able to thoroughly and adaptively record long-range spatio-temporal interactions among the skeletal joints through the use of a Dual-stream Spatio-temporal Transformer (DSTformer) coupled with a motion encoder. Bachelor's degree 2024-05-15T00:55:18Z 2024-05-15T00:55:18Z 2024 Final Year Project (FYP) Zheng, Z. (2024). Human pose estimation using artificial intelligence. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176189 https://hdl.handle.net/10356/176189 en A3252-231 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Human pose estimation
spellingShingle Engineering
Human pose estimation
Zheng, Zhoudong
Human pose estimation using artificial intelligence
description Over the past 10 years, human pose estimation (HPE) using artificial intelligence (AI) has gained more and more attention and been used in a range of applications, like human-computer interaction, motion analysis, healthcare, and security. The optimal goal of HPE is to use input data, such as pictures and movies, to identify the various body parts and create a representation of the human body, such as skeleton and mesh. By leveraging and comparing different state-of-the-art (SOTA) deep learning models, such as convolutional neural networks (CNNs) and transformer-based structures, it is found out that insufficient learning of spatial-temporal correlation results from the prior approaches’ inability to effectively represent each joint’s solid inter-frame relationship. Among the models, MixSTE and its baseline model, VideoPose3D, have innovative idea to solve the problems. Therefore, the purpose of this report is to improve the accuracy as well as robustness of the 3D HPE by improving the current models. The present model is able to thoroughly and adaptively record long-range spatio-temporal interactions among the skeletal joints through the use of a Dual-stream Spatio-temporal Transformer (DSTformer) coupled with a motion encoder.
author2 Yap Kim Hui
author_facet Yap Kim Hui
Zheng, Zhoudong
format Final Year Project
author Zheng, Zhoudong
author_sort Zheng, Zhoudong
title Human pose estimation using artificial intelligence
title_short Human pose estimation using artificial intelligence
title_full Human pose estimation using artificial intelligence
title_fullStr Human pose estimation using artificial intelligence
title_full_unstemmed Human pose estimation using artificial intelligence
title_sort human pose estimation using artificial intelligence
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176189
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