Training convolutional neural networks for human re-identification (B)

Human re-identification has become a popular research topic due to advancements in neural network research and progression in IoT technology, Furthermore, with increasing importance for public security, human re-identification is critical to the security firms and governments. The objective of the...

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Main Author:	Chew, Keng Siang
Other Authors:	Alex Kot Chichung
Format:	Final Year Project
Language:	English
Published:	2019
Subjects:	DRNTU::Engineering::Electrical and electronic engineering
Online Access:	http://hdl.handle.net/10356/77315
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Institution:	Nanyang Technological University
Language:	English

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record_format	dspace
spelling	sg-ntu-dr.10356-773152023-07-07T16:05:05Z Training convolutional neural networks for human re-identification (B) Chew, Keng Siang Alex Kot Chichung School of Electrical and Electronic Engineering Rapid-Rich Object Search (ROSE) Lab DRNTU::Engineering::Electrical and electronic engineering Human re-identification has become a popular research topic due to advancements in neural network research and progression in IoT technology, Furthermore, with increasing importance for public security, human re-identification is critical to the security firms and governments. The objective of the project is to develop a dataset of images from real-world based security cameras and implement the latest neural models to the dataset to evaluate their performance and comparing them with available public datasets. SoftMax and triplet loss models will be implemented to evaluate the results, as well as the implementation of data augmentation method for further evaluation and comparison of the datasets. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-05-27T02:17:16Z 2019-05-27T02:17:16Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77315 en Nanyang Technological University 54 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::Electrical and electronic engineering
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering Chew, Keng Siang Training convolutional neural networks for human re-identification (B)
description	Human re-identification has become a popular research topic due to advancements in neural network research and progression in IoT technology, Furthermore, with increasing importance for public security, human re-identification is critical to the security firms and governments. The objective of the project is to develop a dataset of images from real-world based security cameras and implement the latest neural models to the dataset to evaluate their performance and comparing them with available public datasets. SoftMax and triplet loss models will be implemented to evaluate the results, as well as the implementation of data augmentation method for further evaluation and comparison of the datasets.
author2	Alex Kot Chichung
author_facet	Alex Kot Chichung Chew, Keng Siang
format	Final Year Project
author	Chew, Keng Siang
author_sort	Chew, Keng Siang
title	Training convolutional neural networks for human re-identification (B)
title_short	Training convolutional neural networks for human re-identification (B)
title_full	Training convolutional neural networks for human re-identification (B)
title_fullStr	Training convolutional neural networks for human re-identification (B)
title_full_unstemmed	Training convolutional neural networks for human re-identification (B)
title_sort	training convolutional neural networks for human re-identification (b)
publishDate	2019
url	http://hdl.handle.net/10356/77315
_version_	1772826310267109376

Training convolutional neural networks for human re-identification (B)

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