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...

Full description

Saved in:
Bibliographic Details
Main Author: Mohamed Ali Mohamed Umar
Other Authors: Mohamed M. Sabry Aly
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/181208
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-181208
record_format dspace
spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Engineering
Hand-gesture
Arrtificial intelliigence
Deep learning
Embedded devices
Recognition
spellingShingle 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
title_sort katon: real-time hand-gesture recognition on tiny embedded systems
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/181208
_version_ 1816859060048232448