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Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight
This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rig...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948753/ https://www.ncbi.nlm.nih.gov/pubmed/29690610 http://dx.doi.org/10.3390/s18041292 |
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author | Guo, Siqiu Zhang, Tao Song, Yulong Qian, Feng |
author_facet | Guo, Siqiu Zhang, Tao Song, Yulong Qian, Feng |
author_sort | Guo, Siqiu |
collection | PubMed |
description | This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios. |
format | Online Article Text |
id | pubmed-5948753 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59487532018-05-17 Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight Guo, Siqiu Zhang, Tao Song, Yulong Qian, Feng Sensors (Basel) Article This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios. MDPI 2018-04-23 /pmc/articles/PMC5948753/ /pubmed/29690610 http://dx.doi.org/10.3390/s18041292 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guo, Siqiu Zhang, Tao Song, Yulong Qian, Feng Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight |
title | Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight |
title_full | Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight |
title_fullStr | Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight |
title_full_unstemmed | Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight |
title_short | Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight |
title_sort | color feature-based object tracking through particle swarm optimization with improved inertia weight |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948753/ https://www.ncbi.nlm.nih.gov/pubmed/29690610 http://dx.doi.org/10.3390/s18041292 |
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