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Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters
In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, thei...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713650/ https://www.ncbi.nlm.nih.gov/pubmed/29140311 http://dx.doi.org/10.3390/s17112626 |
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author | Li, Fan Zhang, Sirou Qiao, Xiaoya |
author_facet | Li, Fan Zhang, Sirou Qiao, Xiaoya |
author_sort | Li, Fan |
collection | PubMed |
description | In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view. |
format | Online Article Text |
id | pubmed-5713650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-57136502017-12-07 Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters Li, Fan Zhang, Sirou Qiao, Xiaoya Sensors (Basel) Article In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view. MDPI 2017-11-15 /pmc/articles/PMC5713650/ /pubmed/29140311 http://dx.doi.org/10.3390/s17112626 Text en © 2017 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 Li, Fan Zhang, Sirou Qiao, Xiaoya Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters |
title | Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters |
title_full | Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters |
title_fullStr | Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters |
title_full_unstemmed | Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters |
title_short | Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters |
title_sort | scene-aware adaptive updating for visual tracking via correlation filters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713650/ https://www.ncbi.nlm.nih.gov/pubmed/29140311 http://dx.doi.org/10.3390/s17112626 |
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