Lightweight surveillance systems on embedded domains

This report aims to investigate the development of a human detection machine vision algorithm that is lightweight and optimized for resource-constrained embedded systems. Pre-trained lightweight models as a base to implement real-time human detection on the Himax WE-I plus board as the testbed. The...

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Main Author: Muhammad Nabil Hakeem Bin Kamsani
Other Authors: Mohamed M. Sabry Aly
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/181568
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1815682024-12-10T01:34:52Z Lightweight surveillance systems on embedded domains Muhammad Nabil Hakeem Bin Kamsani Mohamed M. Sabry Aly College of Computing and Data Science msabry@ntu.edu.sg Computer and Information Science This report aims to investigate the development of a human detection machine vision algorithm that is lightweight and optimized for resource-constrained embedded systems. Pre-trained lightweight models as a base to implement real-time human detection on the Himax WE-I plus board as the testbed. The project's main goal is to have a model, which is very lightweight and uses the necessary optimization techniques like quantization and pruning, that has a small model size and has good speed and performance without sacrificing the model's accuracy. Different platforms that can simplify the process of creating the lightweight model were explored. The findings of the project demonstrate that with appropriate optimizations, deep learning models can be implemented on resource-constrained embedded systems which enables applications such as smart surveillance systems to function effectively in real-world environments. Bachelor's degree 2024-12-10T01:34:52Z 2024-12-10T01:34:52Z 2024 Final Year Project (FYP) Muhammad Nabil Hakeem Bin Kamsani (2024). Lightweight surveillance systems on embedded domains. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181568 https://hdl.handle.net/10356/181568 en SCSE23-1142 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
spellingShingle Computer and Information Science
Muhammad Nabil Hakeem Bin Kamsani
Lightweight surveillance systems on embedded domains
description This report aims to investigate the development of a human detection machine vision algorithm that is lightweight and optimized for resource-constrained embedded systems. Pre-trained lightweight models as a base to implement real-time human detection on the Himax WE-I plus board as the testbed. The project's main goal is to have a model, which is very lightweight and uses the necessary optimization techniques like quantization and pruning, that has a small model size and has good speed and performance without sacrificing the model's accuracy. Different platforms that can simplify the process of creating the lightweight model were explored. The findings of the project demonstrate that with appropriate optimizations, deep learning models can be implemented on resource-constrained embedded systems which enables applications such as smart surveillance systems to function effectively in real-world environments.
author2 Mohamed M. Sabry Aly
author_facet Mohamed M. Sabry Aly
Muhammad Nabil Hakeem Bin Kamsani
format Final Year Project
author Muhammad Nabil Hakeem Bin Kamsani
author_sort Muhammad Nabil Hakeem Bin Kamsani
title Lightweight surveillance systems on embedded domains
title_short Lightweight surveillance systems on embedded domains
title_full Lightweight surveillance systems on embedded domains
title_fullStr Lightweight surveillance systems on embedded domains
title_full_unstemmed Lightweight surveillance systems on embedded domains
title_sort lightweight surveillance systems on embedded domains
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/181568
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