Double-Channel Object Tracking With Position Deviation Suppression

The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminati...
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Gespeichert in:

Autor*in:	Jun Chu [verfasserIn] Xuji Tu [verfasserIn] Lu Leng [verfasserIn] Jun Miao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Double-channel object tracking position deviation suppression DCF MDNet

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 856-866
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:856-866

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2961778

Katalog-ID:	DOAJ056274483

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520			\|a The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016.
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allfields	10.1109/ACCESS.2019.2961778 doi (DE-627)DOAJ056274483 (DE-599)DOAJ507618d70fe6451d92125a796a6d8a6c DE-627 ger DE-627 rakwb eng TK1-9971 Jun Chu verfasserin aut Double-Channel Object Tracking With Position Deviation Suppression 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016. Double-channel object tracking position deviation suppression DCF MDNet Electrical engineering. Electronics. Nuclear engineering Xuji Tu verfasserin aut Lu Leng verfasserin aut Jun Miao verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 856-866 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:856-866 https://doi.org/10.1109/ACCESS.2019.2961778 kostenfrei https://doaj.org/article/507618d70fe6451d92125a796a6d8a6c kostenfrei https://ieeexplore.ieee.org/document/8939361/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 856-866
spelling	10.1109/ACCESS.2019.2961778 doi (DE-627)DOAJ056274483 (DE-599)DOAJ507618d70fe6451d92125a796a6d8a6c DE-627 ger DE-627 rakwb eng TK1-9971 Jun Chu verfasserin aut Double-Channel Object Tracking With Position Deviation Suppression 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016. Double-channel object tracking position deviation suppression DCF MDNet Electrical engineering. Electronics. Nuclear engineering Xuji Tu verfasserin aut Lu Leng verfasserin aut Jun Miao verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 856-866 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:856-866 https://doi.org/10.1109/ACCESS.2019.2961778 kostenfrei https://doaj.org/article/507618d70fe6451d92125a796a6d8a6c kostenfrei https://ieeexplore.ieee.org/document/8939361/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 856-866
allfields_unstemmed	10.1109/ACCESS.2019.2961778 doi (DE-627)DOAJ056274483 (DE-599)DOAJ507618d70fe6451d92125a796a6d8a6c DE-627 ger DE-627 rakwb eng TK1-9971 Jun Chu verfasserin aut Double-Channel Object Tracking With Position Deviation Suppression 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016. Double-channel object tracking position deviation suppression DCF MDNet Electrical engineering. Electronics. Nuclear engineering Xuji Tu verfasserin aut Lu Leng verfasserin aut Jun Miao verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 856-866 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:856-866 https://doi.org/10.1109/ACCESS.2019.2961778 kostenfrei https://doaj.org/article/507618d70fe6451d92125a796a6d8a6c kostenfrei https://ieeexplore.ieee.org/document/8939361/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 856-866
allfieldsGer	10.1109/ACCESS.2019.2961778 doi (DE-627)DOAJ056274483 (DE-599)DOAJ507618d70fe6451d92125a796a6d8a6c DE-627 ger DE-627 rakwb eng TK1-9971 Jun Chu verfasserin aut Double-Channel Object Tracking With Position Deviation Suppression 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016. Double-channel object tracking position deviation suppression DCF MDNet Electrical engineering. Electronics. Nuclear engineering Xuji Tu verfasserin aut Lu Leng verfasserin aut Jun Miao verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 856-866 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:856-866 https://doi.org/10.1109/ACCESS.2019.2961778 kostenfrei https://doaj.org/article/507618d70fe6451d92125a796a6d8a6c kostenfrei https://ieeexplore.ieee.org/document/8939361/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 856-866
allfieldsSound	10.1109/ACCESS.2019.2961778 doi (DE-627)DOAJ056274483 (DE-599)DOAJ507618d70fe6451d92125a796a6d8a6c DE-627 ger DE-627 rakwb eng TK1-9971 Jun Chu verfasserin aut Double-Channel Object Tracking With Position Deviation Suppression 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016. Double-channel object tracking position deviation suppression DCF MDNet Electrical engineering. Electronics. Nuclear engineering Xuji Tu verfasserin aut Lu Leng verfasserin aut Jun Miao verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 856-866 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:856-866 https://doi.org/10.1109/ACCESS.2019.2961778 kostenfrei https://doaj.org/article/507618d70fe6451d92125a796a6d8a6c kostenfrei https://ieeexplore.ieee.org/document/8939361/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 856-866
language	English
source	In IEEE Access 8(2020), Seite 856-866 volume:8 year:2020 pages:856-866
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topic_facet	Double-channel object tracking position deviation suppression DCF MDNet Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Jun Chu @@aut@@ Xuji Tu @@aut@@ Lu Leng @@aut@@ Jun Miao @@aut@@
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callnumber-first	T - Technology
author	Jun Chu
spellingShingle	Jun Chu misc TK1-9971 misc Double-channel object tracking misc position deviation suppression misc DCF misc MDNet misc Electrical engineering. Electronics. Nuclear engineering Double-Channel Object Tracking With Position Deviation Suppression
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topic_title	TK1-9971 Double-Channel Object Tracking With Position Deviation Suppression Double-channel object tracking position deviation suppression DCF MDNet
topic	misc TK1-9971 misc Double-channel object tracking misc position deviation suppression misc DCF misc MDNet misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Double-channel object tracking misc position deviation suppression misc DCF misc MDNet misc Electrical engineering. Electronics. Nuclear engineering
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title	Double-Channel Object Tracking With Position Deviation Suppression
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title_full	Double-Channel Object Tracking With Position Deviation Suppression
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title_sort	double-channel object tracking with position deviation suppression
callnumber	TK1-9971
title_auth	Double-Channel Object Tracking With Position Deviation Suppression
abstract	The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016.
abstractGer	The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016.
abstract_unstemmed	The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016.
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title_short	Double-Channel Object Tracking With Position Deviation Suppression
url	https://doi.org/10.1109/ACCESS.2019.2961778 https://doaj.org/article/507618d70fe6451d92125a796a6d8a6c https://ieeexplore.ieee.org/document/8939361/ https://doaj.org/toc/2169-3536
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author2	Xuji Tu Lu Leng Jun Miao
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up_date	2024-07-03T19:53:59.683Z
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