Human re-identification using siamese convolutional neural network on Nvidia Geforce RTX 2060

Human reidentification in multiple cameras with disjoint views is to match a pair of humans appearing in different cameras with non-overlapping views. Human reidentification has been extensively studied in recent years because it plays a significant role in many applications such as human tracking a...

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Main Author:	Rajathurai, Elavarasan
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/98268/1/ElavarasanRajathuraiMSKE2021.pdf
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id	my-utm-ep.98268
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spelling	my-utm-ep.982682022-11-30T04:51:30Z Human re-identification using siamese convolutional neural network on Nvidia Geforce RTX 2060 2021 Rajathurai, Elavarasan TK Electrical engineering. Electronics Nuclear engineering Human reidentification in multiple cameras with disjoint views is to match a pair of humans appearing in different cameras with non-overlapping views. Human reidentification has been extensively studied in recent years because it plays a significant role in many applications such as human tracking and video retrieval. However, human re-identification is a challenging task due to varying factors such as color, pose, viewpoint, lighting conditions, low resolution and partial occlusion. Most of the existing methods in handling human re-identification task are based on various handcrafted features and metric learning. However, hand-crafted features method requires expert knowledge and requires a lot of time to tune the features and metric learning methods are not powerful enough to exploit the nonlinear relationship of samples. The main objective of this thesis is to implement Siamese Convolutional Neural Network (SCNN) for person re-identification task in multiple cameras on the NVIDIA® GeForce RTX™ 2060 platform, including person detection. This continuous with validation of the applicability of SCNN and compare with existing techniques. In this work, global and local features of human images are extracted from SCNN. The proposed SCNN consists of two identical Convolution Neural Networks with common parameters that can automatically learn hierarchical feature representations from image pixels directly which has advantages than the hand-crafted design and metric learning method. Experiments were conducted with CUHK02 offline database with non-overlapping cameras. The proposed technique demonstrated a person re-identification using SCNN on the NVIDIA® GeForce RTX™ 2060 platform. 2021 Thesis http://eprints.utm.my/id/eprint/98268/ http://eprints.utm.my/id/eprint/98268/1/ElavarasanRajathuraiMSKE2021.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:144560 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Rajathurai, Elavarasan Human re-identification using siamese convolutional neural network on Nvidia Geforce RTX 2060
description	Human reidentification in multiple cameras with disjoint views is to match a pair of humans appearing in different cameras with non-overlapping views. Human reidentification has been extensively studied in recent years because it plays a significant role in many applications such as human tracking and video retrieval. However, human re-identification is a challenging task due to varying factors such as color, pose, viewpoint, lighting conditions, low resolution and partial occlusion. Most of the existing methods in handling human re-identification task are based on various handcrafted features and metric learning. However, hand-crafted features method requires expert knowledge and requires a lot of time to tune the features and metric learning methods are not powerful enough to exploit the nonlinear relationship of samples. The main objective of this thesis is to implement Siamese Convolutional Neural Network (SCNN) for person re-identification task in multiple cameras on the NVIDIA® GeForce RTX™ 2060 platform, including person detection. This continuous with validation of the applicability of SCNN and compare with existing techniques. In this work, global and local features of human images are extracted from SCNN. The proposed SCNN consists of two identical Convolution Neural Networks with common parameters that can automatically learn hierarchical feature representations from image pixels directly which has advantages than the hand-crafted design and metric learning method. Experiments were conducted with CUHK02 offline database with non-overlapping cameras. The proposed technique demonstrated a person re-identification using SCNN on the NVIDIA® GeForce RTX™ 2060 platform.
format	Thesis
qualification_level	Master's degree
author	Rajathurai, Elavarasan
author_facet	Rajathurai, Elavarasan
author_sort	Rajathurai, Elavarasan
title	Human re-identification using siamese convolutional neural network on Nvidia Geforce RTX 2060
title_short	Human re-identification using siamese convolutional neural network on Nvidia Geforce RTX 2060
title_full	Human re-identification using siamese convolutional neural network on Nvidia Geforce RTX 2060
title_fullStr	Human re-identification using siamese convolutional neural network on Nvidia Geforce RTX 2060
title_full_unstemmed	Human re-identification using siamese convolutional neural network on Nvidia Geforce RTX 2060
title_sort	human re-identification using siamese convolutional neural network on nvidia geforce rtx 2060
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/98268/1/ElavarasanRajathuraiMSKE2021.pdf
_version_	1776100570532151296

Human re-identification using siamese convolutional neural network on Nvidia Geforce RTX 2060

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