An enhancement particle-based method for dynamic object tracking

Camera tracking systems have become a common requirement in today‘s society. The availability of high quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. Generally, it is not easy to track human behavi...

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Main Author:	Zalili, Musa
Format:	Thesis
Language:	English
Published:	2016
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/18193/1/An%20enhancement%20particle-based%20method%20for%20dynamic%20object%20tracking.pdf
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id	my-ump-ir.18193
record_format	uketd_dc
spelling	my-ump-ir.181932023-03-02T07:11:06Z An enhancement particle-based method for dynamic object tracking 2016-10 Zalili, Musa QA75 Electronic computers. Computer science Camera tracking systems have become a common requirement in today‘s society. The availability of high quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. Generally, it is not easy to track human behavior in an environment with a large view. This study aims to address three problems associated with object tracking. The first problem to be considered in this study is to improve the accuracy of object detection for multiple targets in nonlinear motion and during the occlusions occurs. Secondly, to track the precise location of object in relative size. The third problem to be considered is a to improve the processing time for the process of object detection and tracking. Thus, to address the accuracy of object detection, we proposed a new method of dynamic template matching using Global best Local Neighborhood in Particle Swarm Optimization (GbLN-PSO). In this study, feature-based approach using a GbLN-PSO algorithm will be applied to search the minimum value of dynamic template matching process. Furthermore, a model-based particle filter is used to address the problem of tracking objects precisely. This method is able to predict the precise location of object movement in the 2-D image. The combination of these two new proposed solutions, consequently, will improve the processing time in detecting the object with precision location. The proposed method has been tested with an experimental module using several sets of video data provided by the Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) and two other video streams of UBC hockey and Malaysian football games. The experiment has shown that the accuracy of tracking performance has increased up to 25% compared to others reported work in the scientific literature. 2016-10 Thesis http://umpir.ump.edu.my/id/eprint/18193/ http://umpir.ump.edu.my/id/eprint/18193/1/An%20enhancement%20particle-based%20method%20for%20dynamic%20object%20tracking.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Computer Systems and Software Engineering Abu Bakar, Rohani
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Abu Bakar, Rohani
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Zalili, Musa An enhancement particle-based method for dynamic object tracking
description	Camera tracking systems have become a common requirement in today‘s society. The availability of high quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. Generally, it is not easy to track human behavior in an environment with a large view. This study aims to address three problems associated with object tracking. The first problem to be considered in this study is to improve the accuracy of object detection for multiple targets in nonlinear motion and during the occlusions occurs. Secondly, to track the precise location of object in relative size. The third problem to be considered is a to improve the processing time for the process of object detection and tracking. Thus, to address the accuracy of object detection, we proposed a new method of dynamic template matching using Global best Local Neighborhood in Particle Swarm Optimization (GbLN-PSO). In this study, feature-based approach using a GbLN-PSO algorithm will be applied to search the minimum value of dynamic template matching process. Furthermore, a model-based particle filter is used to address the problem of tracking objects precisely. This method is able to predict the precise location of object movement in the 2-D image. The combination of these two new proposed solutions, consequently, will improve the processing time in detecting the object with precision location. The proposed method has been tested with an experimental module using several sets of video data provided by the Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) and two other video streams of UBC hockey and Malaysian football games. The experiment has shown that the accuracy of tracking performance has increased up to 25% compared to others reported work in the scientific literature.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zalili, Musa
author_facet	Zalili, Musa
author_sort	Zalili, Musa
title	An enhancement particle-based method for dynamic object tracking
title_short	An enhancement particle-based method for dynamic object tracking
title_full	An enhancement particle-based method for dynamic object tracking
title_fullStr	An enhancement particle-based method for dynamic object tracking
title_full_unstemmed	An enhancement particle-based method for dynamic object tracking
title_sort	enhancement particle-based method for dynamic object tracking
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computer Systems and Software Engineering
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/18193/1/An%20enhancement%20particle-based%20method%20for%20dynamic%20object%20tracking.pdf
_version_	1783732031678578688

An enhancement particle-based method for dynamic object tracking

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