Modelling and estimation of vehicle tracking using and improved particle filter

This research focuses on reducing the particle size in the resampling stage of the particle filter approach by tracking a single vehicle with overlapping situation. Particle filter is competent to robustly tracking the vehicle under various situations. The vehicle can be tracked by estimating the po...

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Main Author:	Khong, Wei Leong
Format:	Thesis
Language:	English English
Published:	2013
Subjects:	TK5101-6720 Telecommunication Including telegraphy telephone radio radar television
Online Access:	https://eprints.ums.edu.my/id/eprint/41460/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/41460/2/FULLTEXT.pdf
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id	my-ums-ep.41460
record_format	uketd_dc
spelling	my-ums-ep.414602024-11-11T01:13:44Z Modelling and estimation of vehicle tracking using and improved particle filter 2013 Khong, Wei Leong TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television This research focuses on reducing the particle size in the resampling stage of the particle filter approach by tracking a single vehicle with overlapping situation. Particle filter is competent to robustly tracking the vehicle under various situations. The vehicle can be tracked by estimating the position of the target vehicle with a set of distributed random particles with associated weight. Since the estimated position is computed based on the mean value of the hypotheses, the accuracy and efficiency of the particle filter are greatly affected by the particle size. Besides, the placement of the particles also plays an important role in producing accurate tracking results. In practice, the conventional particle filter is facing the particle degeneracy problem after a few iteration of the estimation process. Although the resampling stage in particle filter can overcome the particle degeneracy problem, the large number of particles required to resample is uncertain due to the encountered occlusion situation. Hence, a genetic algorithm based resampling technique will be embedded into the particle filter algorithm to reduce the amount of the resampling particles and subsequently reduce the particle size. Based on the nature of the genetic algorithm, a better estimation of position of the target vehicle can be obtained by recombining the information between the particles. With the improvement of the particle placement, the number of particles used in the resampling stage can be reduced and hence decrease the iteration of the resampling process. Results show that the particle filter with genetic algorithm resampling has successfully reduced 45.5 % of the particles in the resampling stage before the target vehicle is fully occluded by the obstacle vehicle. Subsequently, the developed algorithm has also reduced 50.2 % of the resampling particles when the target vehicle reappears but still partially occluded from the occlusion as compared to the fundamental resampling approach. 2013 Thesis https://eprints.ums.edu.my/id/eprint/41460/ https://eprints.ums.edu.my/id/eprint/41460/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/41460/2/FULLTEXT.pdf text en validuser masters Universiti Malaysia Sabah Sekolah Kejuruteraan dan Teknologi Maklumat
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	TK5101-6720 Telecommunication Including telegraphy telephone radio radar television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy telephone radio radar television Khong, Wei Leong Modelling and estimation of vehicle tracking using and improved particle filter
description	This research focuses on reducing the particle size in the resampling stage of the particle filter approach by tracking a single vehicle with overlapping situation. Particle filter is competent to robustly tracking the vehicle under various situations. The vehicle can be tracked by estimating the position of the target vehicle with a set of distributed random particles with associated weight. Since the estimated position is computed based on the mean value of the hypotheses, the accuracy and efficiency of the particle filter are greatly affected by the particle size. Besides, the placement of the particles also plays an important role in producing accurate tracking results. In practice, the conventional particle filter is facing the particle degeneracy problem after a few iteration of the estimation process. Although the resampling stage in particle filter can overcome the particle degeneracy problem, the large number of particles required to resample is uncertain due to the encountered occlusion situation. Hence, a genetic algorithm based resampling technique will be embedded into the particle filter algorithm to reduce the amount of the resampling particles and subsequently reduce the particle size. Based on the nature of the genetic algorithm, a better estimation of position of the target vehicle can be obtained by recombining the information between the particles. With the improvement of the particle placement, the number of particles used in the resampling stage can be reduced and hence decrease the iteration of the resampling process. Results show that the particle filter with genetic algorithm resampling has successfully reduced 45.5 % of the particles in the resampling stage before the target vehicle is fully occluded by the obstacle vehicle. Subsequently, the developed algorithm has also reduced 50.2 % of the resampling particles when the target vehicle reappears but still partially occluded from the occlusion as compared to the fundamental resampling approach.
format	Thesis
qualification_level	Master's degree
author	Khong, Wei Leong
author_facet	Khong, Wei Leong
author_sort	Khong, Wei Leong
title	Modelling and estimation of vehicle tracking using and improved particle filter
title_short	Modelling and estimation of vehicle tracking using and improved particle filter
title_full	Modelling and estimation of vehicle tracking using and improved particle filter
title_fullStr	Modelling and estimation of vehicle tracking using and improved particle filter
title_full_unstemmed	Modelling and estimation of vehicle tracking using and improved particle filter
title_sort	modelling and estimation of vehicle tracking using and improved particle filter
granting_institution	Universiti Malaysia Sabah
granting_department	Sekolah Kejuruteraan dan Teknologi Maklumat
publishDate	2013
url	https://eprints.ums.edu.my/id/eprint/41460/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/41460/2/FULLTEXT.pdf
_version_	1818611407463120896

Modelling and estimation of vehicle tracking using and improved particle filter

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