Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods

Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods

In this report, the author examines different machine learning methods that aids in crowd counting in the novel context of a fixed location within NTU. The author aims to create an end-to-end solution by creating a self-made dataset and then testing it against contemporary ML models. As privacy is a...

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Main Author: Tan, Mitchell Ming Kai
Other Authors: Dusit Niyato
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Engineering::Mechanical engineering
Online Access:https://hdl.handle.net/10356/151006
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-151006
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spelling sg-ntu-dr.10356-1510062021-06-15T06:15:53Z Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods Tan, Mitchell Ming Kai Dusit Niyato Low Kin Huat School of Mechanical and Aerospace Engineering MKHLOW@ntu.edu.sg, DNIYATO@ntu.edu.sg Engineering::Mechanical engineering In this report, the author examines different machine learning methods that aids in crowd counting in the novel context of a fixed location within NTU. The author aims to create an end-to-end solution by creating a self-made dataset and then testing it against contemporary ML models. As privacy is also a top concern that comes to mind for consumers, Federated Learning comes into play within this project. The author will conduct a quick treatment of which Federated algorithm should be used over the novel ones proposed by the scientific community. Lastly, the author attempts to convert the chosen crowd counting model into a mobile lite and federated model for the unique application within NTU. Bachelor of Engineering (Mechanical Engineering) 2021-06-15T06:15:53Z 2021-06-15T06:15:53Z 2021 Final Year Project (FYP) Tan, M. M. K. (2021). Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/151006 https://hdl.handle.net/10356/151006 en B377 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 Engineering::Mechanical engineering
spellingShingle Engineering::Mechanical engineering
Tan, Mitchell Ming Kai
Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods
description In this report, the author examines different machine learning methods that aids in crowd counting in the novel context of a fixed location within NTU. The author aims to create an end-to-end solution by creating a self-made dataset and then testing it against contemporary ML models. As privacy is also a top concern that comes to mind for consumers, Federated Learning comes into play within this project. The author will conduct a quick treatment of which Federated algorithm should be used over the novel ones proposed by the scientific community. Lastly, the author attempts to convert the chosen crowd counting model into a mobile lite and federated model for the unique application within NTU.
author2 Dusit Niyato
author_facet Dusit Niyato
Tan, Mitchell Ming Kai
format Final Year Project
author Tan, Mitchell Ming Kai
author_sort Tan, Mitchell Ming Kai
title Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods
title_short Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods
title_full Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods
title_fullStr Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods
title_full_unstemmed Indoor UAV crowd investigation part 2 via computer vision applications and federated learning methods
title_sort indoor uav crowd investigation part 2 via computer vision applications and federated learning methods
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/151006
_version_ 1703971148559024128

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