Smart object counter

Crowd counting has been a prevalent problem in the field of machine learning. There have been numerous researches done to improve upon the accuracy of existing solutions. This project aims to incorporate Crowd counting into the context of Universities to provide students with a more accurate estimat...

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Main Author: Ting, Rachel Jie Yi
Other Authors: Loke Yuan Ren
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/137919
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1379192020-04-18T05:09:59Z Smart object counter Ting, Rachel Jie Yi Loke Yuan Ren School of Computer Science and Engineering yrloke@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Crowd counting has been a prevalent problem in the field of machine learning. There have been numerous researches done to improve upon the accuracy of existing solutions. This project aims to incorporate Crowd counting into the context of Universities to provide students with a more accurate estimation of the number of people at various locations around the campus. This would allow users to make better informed choices and plans on when and where to head to. For this project, the images are taken from the NTU website which displays the CCTV images of several locations around the NTU campus. Deep Learning libraries in python are used to estimate the number of people in those images and make a forecast of the crowd count in the future. A few examples of the libraries that will be used in this project would be Pytorch and Theano. The required data would then be stored in a Firebase Realtime database to allow the web application to retrieve the data easily and efficiently. For the front end, HTML programming is used to design the user interface, whereas JavaScript (JS) is used to control the behaviour of the web application. After analysing the results, the crowd estimation for the various locations are largely accurate. However, the estimation at a few of the locations during night-time does not correspond with the expected count. Forecasting proved to be fairly accurate and is able to display the expected crowd behaviour throughout the day, though the predicted count might differ from actual. Overall, the objectives of the project were met with considerable success. However, there are still further improvements that can be made to increase the accuracy and functionality of the application. Bachelor of Engineering (Computer Science) 2020-04-18T05:09:59Z 2020-04-18T05:09:59Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/137919 en SCSE19-0109 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Ting, Rachel Jie Yi
Smart object counter
description Crowd counting has been a prevalent problem in the field of machine learning. There have been numerous researches done to improve upon the accuracy of existing solutions. This project aims to incorporate Crowd counting into the context of Universities to provide students with a more accurate estimation of the number of people at various locations around the campus. This would allow users to make better informed choices and plans on when and where to head to. For this project, the images are taken from the NTU website which displays the CCTV images of several locations around the NTU campus. Deep Learning libraries in python are used to estimate the number of people in those images and make a forecast of the crowd count in the future. A few examples of the libraries that will be used in this project would be Pytorch and Theano. The required data would then be stored in a Firebase Realtime database to allow the web application to retrieve the data easily and efficiently. For the front end, HTML programming is used to design the user interface, whereas JavaScript (JS) is used to control the behaviour of the web application. After analysing the results, the crowd estimation for the various locations are largely accurate. However, the estimation at a few of the locations during night-time does not correspond with the expected count. Forecasting proved to be fairly accurate and is able to display the expected crowd behaviour throughout the day, though the predicted count might differ from actual. Overall, the objectives of the project were met with considerable success. However, there are still further improvements that can be made to increase the accuracy and functionality of the application.
author2 Loke Yuan Ren
author_facet Loke Yuan Ren
Ting, Rachel Jie Yi
format Final Year Project
author Ting, Rachel Jie Yi
author_sort Ting, Rachel Jie Yi
title Smart object counter
title_short Smart object counter
title_full Smart object counter
title_fullStr Smart object counter
title_full_unstemmed Smart object counter
title_sort smart object counter
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/137919
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