Enhanced Automated Framework For Cattle Tracking And Classification

Employing computer vision-based methods in monitoring individual cows has become what researchers are striving for. Computer vision-based methods could be used to monitor each individual cows. The accuracy of the existing methods and frameworks is below expectation in handling these tasks. Moreover,...

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Main Author:	Williams, Bello Rotimi
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/59675/1/24%20Pages%20from%20BELLO%20ROTIMI-WILLIAMS%20-%20TESIS.pdf
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id	my-usm-ep.59675
record_format	uketd_dc
spelling	my-usm-ep.596752023-12-14T01:20:56Z Enhanced Automated Framework For Cattle Tracking And Classification 2022-09 Williams, Bello Rotimi QA75.5-76.95 Electronic computers. Computer science Employing computer vision-based methods in monitoring individual cows has become what researchers are striving for. Computer vision-based methods could be used to monitor each individual cows. The accuracy of the existing methods and frameworks is below expectation in handling these tasks. Moreover, they can still be improved to achieve better and more accurate results. The goal of this research is to provide a framework for better cattle tracking and classification systems. An enhanced object tracking algorithm (PFtmM) that integrates enhanced particle filter algorithm (PFtm) with mean-shift tracker (M) is proposed and deployed as first step to address the problems arise due to occurrence of occlusion and non-linear movement of cow objects in video. The integration of particle filter with mean-shift tracker considers the following techniques: (1) temporary memory for keeping tracks of occluded cow objects; (2) supplementing each algorithm’s weakness by the strength of the other for tracking non-linear movement. Strength of particle filter (PF) is its non-linearity property which it uses to track object’s non-linear movement but, with high computational time and search range as its weakness. Temporary memory (tm) strength is its ability to track full occlusion with reduced computational time and search range. Mean-shift strength is its sensitivity to object’s movement and colour distribution by using similarity function but, with inability to track object’s non-linear movement and full occlusion as its weakness. 2022-09 Thesis http://eprints.usm.my/59675/ http://eprints.usm.my/59675/1/24%20Pages%20from%20BELLO%20ROTIMI-WILLIAMS%20-%20TESIS.pdf application/pdf en public phd doctoral Perpustakaan Hamzah Sendut Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Williams, Bello Rotimi Enhanced Automated Framework For Cattle Tracking And Classification
description	Employing computer vision-based methods in monitoring individual cows has become what researchers are striving for. Computer vision-based methods could be used to monitor each individual cows. The accuracy of the existing methods and frameworks is below expectation in handling these tasks. Moreover, they can still be improved to achieve better and more accurate results. The goal of this research is to provide a framework for better cattle tracking and classification systems. An enhanced object tracking algorithm (PFtmM) that integrates enhanced particle filter algorithm (PFtm) with mean-shift tracker (M) is proposed and deployed as first step to address the problems arise due to occurrence of occlusion and non-linear movement of cow objects in video. The integration of particle filter with mean-shift tracker considers the following techniques: (1) temporary memory for keeping tracks of occluded cow objects; (2) supplementing each algorithm’s weakness by the strength of the other for tracking non-linear movement. Strength of particle filter (PF) is its non-linearity property which it uses to track object’s non-linear movement but, with high computational time and search range as its weakness. Temporary memory (tm) strength is its ability to track full occlusion with reduced computational time and search range. Mean-shift strength is its sensitivity to object’s movement and colour distribution by using similarity function but, with inability to track object’s non-linear movement and full occlusion as its weakness.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Williams, Bello Rotimi
author_facet	Williams, Bello Rotimi
author_sort	Williams, Bello Rotimi
title	Enhanced Automated Framework For Cattle Tracking And Classification
title_short	Enhanced Automated Framework For Cattle Tracking And Classification
title_full	Enhanced Automated Framework For Cattle Tracking And Classification
title_fullStr	Enhanced Automated Framework For Cattle Tracking And Classification
title_full_unstemmed	Enhanced Automated Framework For Cattle Tracking And Classification
title_sort	enhanced automated framework for cattle tracking and classification
granting_institution	Perpustakaan Hamzah Sendut
granting_department	Pusat Pengajian Sains Komputer
publishDate	2022
url	http://eprints.usm.my/59675/1/24%20Pages%20from%20BELLO%20ROTIMI-WILLIAMS%20-%20TESIS.pdf
_version_	1794024032714096640

Enhanced Automated Framework For Cattle Tracking And Classification

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