Security surveillance robot using machine learning techniques

The aim of this project is to design a security surveillance robot target at general home users. Demand for surveillance using edge devices has surged due to end user’s need for security system to deliver convenience, cost effectiveness and privacy protection. This project loads Raspberry Pi 3 Model...

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Main Author: Chien, Yun Ting
Other Authors: Huang Guangbin
Format: Final Year Project
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/75351
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-753512023-07-07T16:18:12Z Security surveillance robot using machine learning techniques Chien, Yun Ting Huang Guangbin School of Electrical and Electronic Engineering DRNTU::Engineering DRNTU::Engineering::Electrical and electronic engineering The aim of this project is to design a security surveillance robot target at general home users. Demand for surveillance using edge devices has surged due to end user’s need for security system to deliver convenience, cost effectiveness and privacy protection. This project loads Raspberry Pi 3 Model B with You Only Look Once (YOLO) model to allow speedy real-time image processing. The CNN model provides a 45 fps processing rate which is close to real time. A robot has been built to carry the Raspberry Pi 3. This would empower the powerful microcomputer with swift mobility. This Security Surveillance Robot can roam about in user household to monitor behaviours and activities. It detects events and recognize pre-trained behaviours. Upon analysis of the recognized anomalies from normal living, end users will be notified by mobile device. The light weight model loaded onto the robot eliminate the need to upload user’s data to the service providers. Data capture as well as analysis will all be done on the robot, locally. Bachelor of Engineering 2018-05-31T01:11:00Z 2018-05-31T01:11:00Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75351 en Nanyang Technological University 51 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering
DRNTU::Engineering::Electrical and electronic engineering
Chien, Yun Ting
Security surveillance robot using machine learning techniques
description The aim of this project is to design a security surveillance robot target at general home users. Demand for surveillance using edge devices has surged due to end user’s need for security system to deliver convenience, cost effectiveness and privacy protection. This project loads Raspberry Pi 3 Model B with You Only Look Once (YOLO) model to allow speedy real-time image processing. The CNN model provides a 45 fps processing rate which is close to real time. A robot has been built to carry the Raspberry Pi 3. This would empower the powerful microcomputer with swift mobility. This Security Surveillance Robot can roam about in user household to monitor behaviours and activities. It detects events and recognize pre-trained behaviours. Upon analysis of the recognized anomalies from normal living, end users will be notified by mobile device. The light weight model loaded onto the robot eliminate the need to upload user’s data to the service providers. Data capture as well as analysis will all be done on the robot, locally.
author2 Huang Guangbin
author_facet Huang Guangbin
Chien, Yun Ting
format Final Year Project
author Chien, Yun Ting
author_sort Chien, Yun Ting
title Security surveillance robot using machine learning techniques
title_short Security surveillance robot using machine learning techniques
title_full Security surveillance robot using machine learning techniques
title_fullStr Security surveillance robot using machine learning techniques
title_full_unstemmed Security surveillance robot using machine learning techniques
title_sort security surveillance robot using machine learning techniques
publishDate 2018
url http://hdl.handle.net/10356/75351
_version_ 1772826488098258944