Human re-identification using deep learning algorithm

The rise of external threats from terrorism attacks upon infrastructures and businesses has led to the widespread usage and reliance on strong and robust surveillance and security systems. Human Re-identification is defined as the process of reidentifying the person of interest through different...

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Main Author: Low, De Wei
Other Authors: Teoh Eam Khwang
Format: Final Year Project
Language:English
Published: 2017
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Online Access:http://hdl.handle.net/10356/71886
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-718862023-07-07T16:49:05Z Human re-identification using deep learning algorithm Low, De Wei Teoh Eam Khwang School of Electrical and Electronic Engineering A*STAR DRNTU::Engineering::Electrical and electronic engineering The rise of external threats from terrorism attacks upon infrastructures and businesses has led to the widespread usage and reliance on strong and robust surveillance and security systems. Human Re-identification is defined as the process of reidentifying the person of interest through different camera or surveillance systems. Human Re-identification has been widely used in surveillance systems worldwide to allow better surveillance and tracking of suspicious identities through different surveillance and camera systems. Automated human re-identification systems reduce the need for manpower and also the time and cost required to train them. However automated human re-identification remains a challenge as the application of these systems is at densely populated area like the airport, shopping malls and places of interest. This gives rise to problems such as occlusion of the person of interest which prevent the system from accurately identify the person of interest. Poor resolution of the different cameras used and low lighting and illumination of surroundings will also affect the robustness and accuracy of the human re-identification system. This final year project seeks to apply and extend the use of deep learning neural network to solve the challenges faced in human re-identification. This project will also be exploring the usage of different colour spaces and the number of colour channels as features to be learned by the neural network. The project will be spilt into three main phases. The first phase will be conducted by using different colour spaces like hue and grayscale from image pairs to form 2- channel colour space positive and negative images. The prepared images will be fed into benchmark convolution neural network architecture for model training. The second phase of the project will be exploring the usage of more colour channels as input to the convolution neural network. The second phase will use the RGB components of the paired images to form a six channel RGB image. The third phase will use HSV colour space components as paired images to form a six-channel HSV to be fed into the convolution neural network for learning. Results collected from the three main phases will be discussed. Bachelor of Engineering 2017-05-19T07:39:12Z 2017-05-19T07:39:12Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/71886 en Nanyang Technological University 98 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
Low, De Wei
Human re-identification using deep learning algorithm
description The rise of external threats from terrorism attacks upon infrastructures and businesses has led to the widespread usage and reliance on strong and robust surveillance and security systems. Human Re-identification is defined as the process of reidentifying the person of interest through different camera or surveillance systems. Human Re-identification has been widely used in surveillance systems worldwide to allow better surveillance and tracking of suspicious identities through different surveillance and camera systems. Automated human re-identification systems reduce the need for manpower and also the time and cost required to train them. However automated human re-identification remains a challenge as the application of these systems is at densely populated area like the airport, shopping malls and places of interest. This gives rise to problems such as occlusion of the person of interest which prevent the system from accurately identify the person of interest. Poor resolution of the different cameras used and low lighting and illumination of surroundings will also affect the robustness and accuracy of the human re-identification system. This final year project seeks to apply and extend the use of deep learning neural network to solve the challenges faced in human re-identification. This project will also be exploring the usage of different colour spaces and the number of colour channels as features to be learned by the neural network. The project will be spilt into three main phases. The first phase will be conducted by using different colour spaces like hue and grayscale from image pairs to form 2- channel colour space positive and negative images. The prepared images will be fed into benchmark convolution neural network architecture for model training. The second phase of the project will be exploring the usage of more colour channels as input to the convolution neural network. The second phase will use the RGB components of the paired images to form a six channel RGB image. The third phase will use HSV colour space components as paired images to form a six-channel HSV to be fed into the convolution neural network for learning. Results collected from the three main phases will be discussed.
author2 Teoh Eam Khwang
author_facet Teoh Eam Khwang
Low, De Wei
format Final Year Project
author Low, De Wei
author_sort Low, De Wei
title Human re-identification using deep learning algorithm
title_short Human re-identification using deep learning algorithm
title_full Human re-identification using deep learning algorithm
title_fullStr Human re-identification using deep learning algorithm
title_full_unstemmed Human re-identification using deep learning algorithm
title_sort human re-identification using deep learning algorithm
publishDate 2017
url http://hdl.handle.net/10356/71886
_version_ 1772828228155604992