Crowd counting using Convolutional Neural Network (CNN) / Nur Syazwina A Kadir

Crowd counting refers to estimating the number of people in a location or photograph. Accurate hand counting is made more difficult in highly crowded locations where people may overlap or obscure one another. Crowd counting is essential for numerous applications, such as urban planning, crowd manage...

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Main Author:	A Kadir, Nur Syazwina
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96277/1/96277.pdf
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id	my-uitm-ir.96277
record_format	uketd_dc
spelling	my-uitm-ir.962772024-06-04T07:20:20Z Crowd counting using Convolutional Neural Network (CNN) / Nur Syazwina A Kadir 2024 A Kadir, Nur Syazwina Neural networks (Computer science) Crowd counting refers to estimating the number of people in a location or photograph. Accurate hand counting is made more difficult in highly crowded locations where people may overlap or obscure one another. Crowd counting is essential for numerous applications, such as urban planning, crowd management, safety and security, event management, and retail analytics. It supports decision-making by organizations and authorities on infrastructure planning, emergency response, and resource allocation. Crowd counting in these circumstances poses a variety of challenges, given the intricate spatial arrangements and fluctuating densities that characterize metropolitan areas. Because human counting becomes problematic in heavily populated places, traditional approaches frequently fail to accurately estimate crowd sizes. Convolutional Neural Networks (CNNs) have consequently gained popularity as a potential solution to this problem. The complex patterns found in urban crowd scenes are well-suited for CNNs to capture because they are excellent at extracting hierarchical features from photos. CNNs are capable of learning to estimate crowd counts with remarkably high accuracy by training on massive datasets labelled with densities. This method provides efficiency and scalability while also addressing the shortcomings of traditional approaches. A thorough evaluation was used to figure out the performance of the CNN model. The model produced a Mean Absolute Error (MAE) of 68 during the training phase when it was used with an 80:20 dataset split, wherein 80% of the data was put aside for training and the remaining 20% for testing. The model subsequently demonstrated an MAE of 77 during the testing phase. This evaluation process provides valuable insights into the model's performance across both training and testing datasets, highlighting its predictive capabilities and potential areas for improvement. In tasks involving crowd counting, CNNs have proven to be remarkably accurate. Metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE), which quantify the difference between predicted count and actual crowd counts, are used to measure accuracy. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96277/ https://ir.uitm.edu.my/id/eprint/96277/1/96277.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Media Tan, Gloria Jennis
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Tan, Gloria Jennis
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) A Kadir, Nur Syazwina Crowd counting using Convolutional Neural Network (CNN) / Nur Syazwina A Kadir
description	Crowd counting refers to estimating the number of people in a location or photograph. Accurate hand counting is made more difficult in highly crowded locations where people may overlap or obscure one another. Crowd counting is essential for numerous applications, such as urban planning, crowd management, safety and security, event management, and retail analytics. It supports decision-making by organizations and authorities on infrastructure planning, emergency response, and resource allocation. Crowd counting in these circumstances poses a variety of challenges, given the intricate spatial arrangements and fluctuating densities that characterize metropolitan areas. Because human counting becomes problematic in heavily populated places, traditional approaches frequently fail to accurately estimate crowd sizes. Convolutional Neural Networks (CNNs) have consequently gained popularity as a potential solution to this problem. The complex patterns found in urban crowd scenes are well-suited for CNNs to capture because they are excellent at extracting hierarchical features from photos. CNNs are capable of learning to estimate crowd counts with remarkably high accuracy by training on massive datasets labelled with densities. This method provides efficiency and scalability while also addressing the shortcomings of traditional approaches. A thorough evaluation was used to figure out the performance of the CNN model. The model produced a Mean Absolute Error (MAE) of 68 during the training phase when it was used with an 80:20 dataset split, wherein 80% of the data was put aside for training and the remaining 20% for testing. The model subsequently demonstrated an MAE of 77 during the testing phase. This evaluation process provides valuable insights into the model's performance across both training and testing datasets, highlighting its predictive capabilities and potential areas for improvement. In tasks involving crowd counting, CNNs have proven to be remarkably accurate. Metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE), which quantify the difference between predicted count and actual crowd counts, are used to measure accuracy.
format	Thesis
qualification_level	Bachelor degree
author	A Kadir, Nur Syazwina
author_facet	A Kadir, Nur Syazwina
author_sort	A Kadir, Nur Syazwina
title	Crowd counting using Convolutional Neural Network (CNN) / Nur Syazwina A Kadir
title_short	Crowd counting using Convolutional Neural Network (CNN) / Nur Syazwina A Kadir
title_full	Crowd counting using Convolutional Neural Network (CNN) / Nur Syazwina A Kadir
title_fullStr	Crowd counting using Convolutional Neural Network (CNN) / Nur Syazwina A Kadir
title_full_unstemmed	Crowd counting using Convolutional Neural Network (CNN) / Nur Syazwina A Kadir
title_sort	crowd counting using convolutional neural network (cnn) / nur syazwina a kadir
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Media
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96277/1/96277.pdf
_version_	1804889982270177280

Crowd counting using Convolutional Neural Network (CNN) / Nur Syazwina A Kadir

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