Katon: real-time hand-gesture recognition on tiny embedded systems
As gesture recognition technology transforms human-computer interactions across various applications such as Virtual Reality (VR) and other smart wearables, the demand for real-time gesture recognition on limited-capacity hardware increases. Most of these applications need efficiency and responsi...
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2024
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sg-ntu-dr.10356-1812082024-11-18T03:58:22Z Katon: real-time hand-gesture recognition on tiny embedded systems Mohamed Ali Mohamed Umar Mohamed M. Sabry Aly College of Computing and Data Science msabry@ntu.edu.sg Computer and Information Science Engineering Hand-gesture Arrtificial intelliigence Deep learning Embedded devices Recognition As gesture recognition technology transforms human-computer interactions across various applications such as Virtual Reality (VR) and other smart wearables, the demand for real-time gesture recognition on limited-capacity hardware increases. Most of these applications need efficiency and responsiveness, which highlights the importance of research in lightweight algorithms capable of real-time processing in resource limited areas. This study evaluates various lightweight algorithms for their viability in real-time hand gesture recognition on resource-limited hardware, focusing on the Himax WE-I Development Board. Bachelor's degree 2024-11-18T03:58:21Z 2024-11-18T03:58:21Z 2024 Final Year Project (FYP) Mohamed Ali Mohamed Umar (2024). Katon: real-time hand-gesture recognition on tiny embedded systems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181208 https://hdl.handle.net/10356/181208 en SCSE23-1146 application/pdf Nanyang Technological University |
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Computer and Information Science Engineering Hand-gesture Arrtificial intelliigence Deep learning Embedded devices Recognition |
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Computer and Information Science Engineering Hand-gesture Arrtificial intelliigence Deep learning Embedded devices Recognition Mohamed Ali Mohamed Umar Katon: real-time hand-gesture recognition on tiny embedded systems |
description |
As gesture recognition technology transforms human-computer interactions across various
applications such as Virtual Reality (VR) and other smart wearables, the demand for real-time
gesture recognition on limited-capacity hardware increases. Most of these applications need
efficiency and responsiveness, which highlights the importance of research in lightweight
algorithms capable of real-time processing in resource limited areas. This study evaluates
various lightweight algorithms for their viability in real-time hand gesture recognition on
resource-limited hardware, focusing on the Himax WE-I Development Board. |
author2 |
Mohamed M. Sabry Aly |
author_facet |
Mohamed M. Sabry Aly Mohamed Ali Mohamed Umar |
format |
Final Year Project |
author |
Mohamed Ali Mohamed Umar |
author_sort |
Mohamed Ali Mohamed Umar |
title |
Katon: real-time hand-gesture recognition on tiny embedded systems |
title_short |
Katon: real-time hand-gesture recognition on tiny embedded systems |
title_full |
Katon: real-time hand-gesture recognition on tiny embedded systems |
title_fullStr |
Katon: real-time hand-gesture recognition on tiny embedded systems |
title_full_unstemmed |
Katon: real-time hand-gesture recognition on tiny embedded systems |
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katon: real-time hand-gesture recognition on tiny embedded systems |
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Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/181208 |
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1816859060048232448 |