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A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments

FYP 2 SEM 2 2019/2020

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Main Author: Azreena binti Zaharil Azlan
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Language:English
Published: 2023
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Indoor occupancy detection
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spelling my.uniten.dspace-212852023-05-05T02:47:35Z A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments Azreena binti Zaharil Azlan Indoor occupancy detection FYP 2 SEM 2 2019/2020 Today, the automation of everyday tasks is targeted with smart environment. Smart environment comprises of smart objects that can connect with each other, share information, and coordinate actions in order to take smart and cognitive decisions according to the environment context. This has been made possible with technological advances of external sensors, coupled with computational processing. Smart objects are able to acquire occupancy information. Occupant information is a major input for context-driven control approach in smart environments. However, dealing with dynamic and frequent context changes is a meticulous task. Deep learning support is acquired for real-time data acquisition and processing platforms. This project provides solution for smart environment in classrooms with the objectives to design and model the classroom occupancy detection system, to analyse and fine tuning the real-time occupancy detection, and to investigate the developed real-time system in classroom. This project implements TensorFlow Object Detection API in Python programming for the execution of Convolutional Neural Network (CNN) and OpenCV module to provide real-time domain for the API. The codebase from TensorFlow Object Detection API is revised to have the feature that suits real-time occupancy detection. The approach of the project is obtaining real-time object detection. Next, changing of initialized ID class of object detection to acquire real-time human detection and finally achieving real-time human detection and counter. The output of this project highlights human detection and human counter in classrooms and can be implemented with both image and video input in real-time domain. 2023-05-03T16:27:28Z 2023-05-03T16:27:28Z 2020-02 https://irepository.uniten.edu.my/handle/123456789/21285 en application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
topic Indoor occupancy detection
spellingShingle Indoor occupancy detection
Azreena binti Zaharil Azlan
A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments
description FYP 2 SEM 2 2019/2020
format
author Azreena binti Zaharil Azlan
author_facet Azreena binti Zaharil Azlan
author_sort Azreena binti Zaharil Azlan
title A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments
title_short A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments
title_full A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments
title_fullStr A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments
title_full_unstemmed A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments
title_sort non-intrusive approach for indoor occupancy detection in smart environments
publishDate 2023
_version_ 1806427389935222784
score 13.18916

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