Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking

Particle Swarm Optimization (PSO) has demonstrated its effectiveness in solving the optimization problems. Nevertheless, the PSO algorithm still has the limitation in finding the optimum solution. This is due to the lack of exploration and exploitation of the particle throughout the search space. Th...

Full description

Saved in:

Bibliographic Details
Main Author:	Nurul Izzatie Husna, Muhamad Fauzi
Format:	Thesis
Language:	English
Published:	2023
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/41486/1/ir.NURUL%20IZZATIE%20HUSNA%20-PCC17011.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-ump-ir.41486
record_format	uketd_dc
spelling	my-ump-ir.414862024-06-06T04:27:25Z Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking 2023-09 Nurul Izzatie Husna, Muhamad Fauzi QA75 Electronic computers. Computer science Particle Swarm Optimization (PSO) has demonstrated its effectiveness in solving the optimization problems. Nevertheless, the PSO algorithm still has the limitation in finding the optimum solution. This is due to the lack of exploration and exploitation of the particle throughout the search space. This problem may also cause the premature convergence, the inability to escape the local optima, and has a lack of self-adaptation in their performance. Therefore, a new variant of PSO called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) was proposed to overcome these drawbacks. In this algorithm, the worst particle will be relocating to a new position to ensure the concept of exploration and exploitation remains in the search space. This is the way to avoid the particles from being trapped in local optima and exploit in a suboptimal solution. The concept of exploration will continue when the particle is relocated to a new position. In addition, to evaluate the performance of MEESPSO, we conducted the experiment on 12 benchmark functions. Meanwhile, for the dynamic environment, the method of MEESPSO with Hue, Saturation, Value (HSV)-template matching was proposed to improve the accuracy and precision of object tracking. Based on 12 benchmarks functions, the result shows a slightly better performance in term of convergence, consistency and error rate compared to another algorithm. The experiment for object tracking was conducted in the PETS09 and MOT20 datasets in a crowded environment with occlusion, similar appearance, and deformation challenges. The result demonstrated that the tracking performance of the proposed method was increased by more than 4.67% and 15% in accuracy and precision compared to other reported works. 2023-09 Thesis http://umpir.ump.edu.my/id/eprint/41486/ http://umpir.ump.edu.my/id/eprint/41486/1/ir.NURUL%20IZZATIE%20HUSNA%20-PCC17011.pdf pdf en public phd doctoral Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Zalili, Musa
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Zalili, Musa
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Nurul Izzatie Husna, Muhamad Fauzi Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking
description	Particle Swarm Optimization (PSO) has demonstrated its effectiveness in solving the optimization problems. Nevertheless, the PSO algorithm still has the limitation in finding the optimum solution. This is due to the lack of exploration and exploitation of the particle throughout the search space. This problem may also cause the premature convergence, the inability to escape the local optima, and has a lack of self-adaptation in their performance. Therefore, a new variant of PSO called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) was proposed to overcome these drawbacks. In this algorithm, the worst particle will be relocating to a new position to ensure the concept of exploration and exploitation remains in the search space. This is the way to avoid the particles from being trapped in local optima and exploit in a suboptimal solution. The concept of exploration will continue when the particle is relocated to a new position. In addition, to evaluate the performance of MEESPSO, we conducted the experiment on 12 benchmark functions. Meanwhile, for the dynamic environment, the method of MEESPSO with Hue, Saturation, Value (HSV)-template matching was proposed to improve the accuracy and precision of object tracking. Based on 12 benchmarks functions, the result shows a slightly better performance in term of convergence, consistency and error rate compared to another algorithm. The experiment for object tracking was conducted in the PETS09 and MOT20 datasets in a crowded environment with occlusion, similar appearance, and deformation challenges. The result demonstrated that the tracking performance of the proposed method was increased by more than 4.67% and 15% in accuracy and precision compared to other reported works.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Nurul Izzatie Husna, Muhamad Fauzi
author_facet	Nurul Izzatie Husna, Muhamad Fauzi
author_sort	Nurul Izzatie Husna, Muhamad Fauzi
title	Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking
title_short	Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking
title_full	Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking
title_fullStr	Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking
title_full_unstemmed	Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking
title_sort	midrange exploration exploitation searching particle swarm optimization with hsv-template matching for crowded environment object tracking
granting_institution	Universiti Malaysia Pahang Al-Sultan Abdullah
granting_department	Faculty of Computing
publishDate	2023
url	http://umpir.ump.edu.my/id/eprint/41486/1/ir.NURUL%20IZZATIE%20HUSNA%20-PCC17011.pdf
_version_	1804887980291129344

Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking

Similar Items