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Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking

With the advantages of discriminative correlation filter (DCF) in tracking accuracy and computational efficiency, the DCF-based methods have been widely used in the field of unmanned aerial vehicles (UAV) for target tracking. However, UAV tracking inevitably encounters various challenging scenarios,...

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Autores principales: Zhang, Hong, Li, Yan, Liu, Hanyang, Yuan, Ding, Yang, Yifan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056491/
https://www.ncbi.nlm.nih.gov/pubmed/36991696
http://dx.doi.org/10.3390/s23062980
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author Zhang, Hong
Li, Yan
Liu, Hanyang
Yuan, Ding
Yang, Yifan
author_facet Zhang, Hong
Li, Yan
Liu, Hanyang
Yuan, Ding
Yang, Yifan
author_sort Zhang, Hong
collection PubMed
description With the advantages of discriminative correlation filter (DCF) in tracking accuracy and computational efficiency, the DCF-based methods have been widely used in the field of unmanned aerial vehicles (UAV) for target tracking. However, UAV tracking inevitably encounters various challenging scenarios, such as background clutter, similar target, partial/full occlusion, fast motion, etc. These challenges generally lead to multi-peak interferences in the response map that cause the target drift or even loss. To tackle this problem, a response-consistent and background-suppressed correlation filter is proposed for UAV tracking. First, a response-consistent module is developed, in which two response maps are generated by the filter and the features extracted from adjacent frames. Then, these two responses are kept to be consistent with the response from the previous frame. By utilizing the l2-norm constraint for consistency, this module not only can avoid sudden changes of the target response caused by background interferences but also enables the learned filter to preserve the discriminative ability of the previous filter. Second, a novel background-suppressed module is proposed, which makes the learned filter to be more aware of background information by using an attention mask matrix. With the introduction of this module into the DCF framework, the proposed method can further suppress the response interferences of distractors in the background. Finally, extensive comparative experiments have been conducted on three challenging UAV benchmarks, including UAV123@10fps, DTB70 and UAVDT. Experimental results have proved that our tracker has better tracking performance compared with 22 other state-of-the-art trackers. Moreover, our proposed tracker can run at ∼36 FPS on a single CPU for real-time UAV tracking.
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spelling pubmed-100564912023-03-30 Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking Zhang, Hong Li, Yan Liu, Hanyang Yuan, Ding Yang, Yifan Sensors (Basel) Article With the advantages of discriminative correlation filter (DCF) in tracking accuracy and computational efficiency, the DCF-based methods have been widely used in the field of unmanned aerial vehicles (UAV) for target tracking. However, UAV tracking inevitably encounters various challenging scenarios, such as background clutter, similar target, partial/full occlusion, fast motion, etc. These challenges generally lead to multi-peak interferences in the response map that cause the target drift or even loss. To tackle this problem, a response-consistent and background-suppressed correlation filter is proposed for UAV tracking. First, a response-consistent module is developed, in which two response maps are generated by the filter and the features extracted from adjacent frames. Then, these two responses are kept to be consistent with the response from the previous frame. By utilizing the l2-norm constraint for consistency, this module not only can avoid sudden changes of the target response caused by background interferences but also enables the learned filter to preserve the discriminative ability of the previous filter. Second, a novel background-suppressed module is proposed, which makes the learned filter to be more aware of background information by using an attention mask matrix. With the introduction of this module into the DCF framework, the proposed method can further suppress the response interferences of distractors in the background. Finally, extensive comparative experiments have been conducted on three challenging UAV benchmarks, including UAV123@10fps, DTB70 and UAVDT. Experimental results have proved that our tracker has better tracking performance compared with 22 other state-of-the-art trackers. Moreover, our proposed tracker can run at ∼36 FPS on a single CPU for real-time UAV tracking. MDPI 2023-03-09 /pmc/articles/PMC10056491/ /pubmed/36991696 http://dx.doi.org/10.3390/s23062980 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Hong
Li, Yan
Liu, Hanyang
Yuan, Ding
Yang, Yifan
Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking
title Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking
title_full Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking
title_fullStr Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking
title_full_unstemmed Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking
title_short Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking
title_sort learning response-consistent and background-suppressed correlation filters for real-time uav tracking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056491/
https://www.ncbi.nlm.nih.gov/pubmed/36991696
http://dx.doi.org/10.3390/s23062980
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