Robust Visual Tracking Based on a Modified Flower Pollination Algorithm

In this study, a target tracking algorithm based on the flower pollination algorithm (FPA) is proposed. This method solves the problem of robust visual target tracking in different complex tracking scenes with the good global and local optimisation ability of the FPA. Meanwhile, with the aim of solv...

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Autores principales:	Yuqi Xiao, Yongjun Wu, Fan Yang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Computer vision technology flower pollination algorithm scale adaptive tracker generative tracking method Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/61d52a07867f46bda1ce74e2471a5523
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id	oai:doaj.org-article:61d52a07867f46bda1ce74e2471a5523
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spelling	oai:doaj.org-article:61d52a07867f46bda1ce74e2471a55232021-12-03T00:01:00ZRobust Visual Tracking Based on a Modified Flower Pollination Algorithm2169-353610.1109/ACCESS.2021.3130340https://doaj.org/article/61d52a07867f46bda1ce74e2471a55232021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9625979/https://doaj.org/toc/2169-3536In this study, a target tracking algorithm based on the flower pollination algorithm (FPA) is proposed. This method solves the problem of robust visual target tracking in different complex tracking scenes with the good global and local optimisation ability of the FPA. Meanwhile, with the aim of solving the problem of invalid background feature interference and the loss of effective features caused by the fixed scale of tracking frame in traditional tracking methods, a scale adaptive adjustment model of tracking frame is proposed. Considering that the FPA has good global and local optimization ability at simultaneously, the position update equation of the FPA is introduced as the main optimization method of target tracking. In addition, considering that the traditional FPA is similar to classical swarm intelligence algorithm (such as the particle swarm optimization algorithm), it also faces the problems of a high probability of falling into local extrema, a low efficiency of late convergence speed and a high probability of early maturity. Therefore, this work proposes the GTFPA, an advanced FPA based on the gravitational search algorithm (GSA) and mutation mechanism via a trigonometric function. We qualitatively, quantitatively and statistically compare the proposed method with other classical general tracking methods through two datasets, OTB2015 and VOT2018, which contain hundreds of video sequences and more than ten tracking scenes and can effectively test the success rate, accuracy and stability of the trackers. The results of a large number of tracking experiments in a variety of complex tracking scenarios prove that the proposed GTFPA tracker performs well with regards to efficiency, accuracy and robustness.Yuqi XiaoYongjun WuFan YangIEEEarticleComputer vision technologyflower pollination algorithmscale adaptive trackergenerative tracking methodElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157458-157467 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer vision technology flower pollination algorithm scale adaptive tracker generative tracking method Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Computer vision technology flower pollination algorithm scale adaptive tracker generative tracking method Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yuqi Xiao Yongjun Wu Fan Yang Robust Visual Tracking Based on a Modified Flower Pollination Algorithm
description	In this study, a target tracking algorithm based on the flower pollination algorithm (FPA) is proposed. This method solves the problem of robust visual target tracking in different complex tracking scenes with the good global and local optimisation ability of the FPA. Meanwhile, with the aim of solving the problem of invalid background feature interference and the loss of effective features caused by the fixed scale of tracking frame in traditional tracking methods, a scale adaptive adjustment model of tracking frame is proposed. Considering that the FPA has good global and local optimization ability at simultaneously, the position update equation of the FPA is introduced as the main optimization method of target tracking. In addition, considering that the traditional FPA is similar to classical swarm intelligence algorithm (such as the particle swarm optimization algorithm), it also faces the problems of a high probability of falling into local extrema, a low efficiency of late convergence speed and a high probability of early maturity. Therefore, this work proposes the GTFPA, an advanced FPA based on the gravitational search algorithm (GSA) and mutation mechanism via a trigonometric function. We qualitatively, quantitatively and statistically compare the proposed method with other classical general tracking methods through two datasets, OTB2015 and VOT2018, which contain hundreds of video sequences and more than ten tracking scenes and can effectively test the success rate, accuracy and stability of the trackers. The results of a large number of tracking experiments in a variety of complex tracking scenarios prove that the proposed GTFPA tracker performs well with regards to efficiency, accuracy and robustness.
format	article
author	Yuqi Xiao Yongjun Wu Fan Yang
author_facet	Yuqi Xiao Yongjun Wu Fan Yang
author_sort	Yuqi Xiao
title	Robust Visual Tracking Based on a Modified Flower Pollination Algorithm
title_short	Robust Visual Tracking Based on a Modified Flower Pollination Algorithm
title_full	Robust Visual Tracking Based on a Modified Flower Pollination Algorithm
title_fullStr	Robust Visual Tracking Based on a Modified Flower Pollination Algorithm
title_full_unstemmed	Robust Visual Tracking Based on a Modified Flower Pollination Algorithm
title_sort	robust visual tracking based on a modified flower pollination algorithm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/61d52a07867f46bda1ce74e2471a5523
work_keys_str_mv	AT yuqixiao robustvisualtrackingbasedonamodifiedflowerpollinationalgorithm AT yongjunwu robustvisualtrackingbasedonamodifiedflowerpollinationalgorithm AT fanyang robustvisualtrackingbasedonamodifiedflowerpollinationalgorithm
_version_	1718373990632783872

Robust Visual Tracking Based on a Modified Flower Pollination Algorithm

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