TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos

Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Qiqi Zhu [verfasserIn] Xin Huang [verfasserIn] Qingfeng Guan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Target-aware Object tracking Satellite videos Weaken heatmap Pixel-wise refinement Corner-based tracker

Übergeordnetes Werk:	In: International Journal of Applied Earth Observations and Geoinformation - Elsevier, 2022, 128(2024), Seite 103723-
Übergeordnetes Werk:	volume:128 ; year:2024 ; pages:103723-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jag.2024.103723

Katalog-ID:	DOAJ091688493

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ091688493
003	DE-627
005	20240413235832.0
007	cr uuu---uuuuu
008	240412s2024 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.jag.2024.103723 \|2 doi
035			\|a (DE-627)DOAJ091688493
035			\|a (DE-599)DOAJa21c4d2d270846d48eea6d98b1491db4
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a GB3-5030
050		0	\|a GE1-350
100	0		\|a Qiqi Zhu \|e verfasserin \|4 aut
245	1	0	\|a TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos
264		1	\|c 2024
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios.
650		4	\|a Target-aware
650		4	\|a Object tracking
650		4	\|a Satellite videos
650		4	\|a Weaken heatmap
650		4	\|a Pixel-wise refinement
650		4	\|a Corner-based tracker
653		0	\|a Physical geography
653		0	\|a Environmental sciences
700	0		\|a Xin Huang \|e verfasserin \|4 aut
700	0		\|a Qingfeng Guan \|e verfasserin \|4 aut
773	0	8	\|i In \|t International Journal of Applied Earth Observations and Geoinformation \|d Elsevier, 2022 \|g 128(2024), Seite 103723- \|w (DE-627)359784119 \|w (DE-600)2097960-5 \|x 1872826X \|7 nnns
773	1	8	\|g volume:128 \|g year:2024 \|g pages:103723-
856	4	0	\|u https://doi.org/10.1016/j.jag.2024.103723 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/a21c4d2d270846d48eea6d98b1491db4 \|z kostenfrei
856	4	0	\|u http://www.sciencedirect.com/science/article/pii/S1569843224000773 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1569-8432 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 128 \|j 2024 \|h 103723-

Indexfelder

author_variant	q z qz x h xh q g qg
matchkey_str	article:1872826X:2024----::acntagtwrbltrlntasomrewrfrigebetr
hierarchy_sort_str	2024
callnumber-subject-code	GB
publishDate	2024
allfields	10.1016/j.jag.2024.103723 doi (DE-627)DOAJ091688493 (DE-599)DOAJa21c4d2d270846d48eea6d98b1491db4 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Qiqi Zhu verfasserin aut TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios. Target-aware Object tracking Satellite videos Weaken heatmap Pixel-wise refinement Corner-based tracker Physical geography Environmental sciences Xin Huang verfasserin aut Qingfeng Guan verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103723- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103723- https://doi.org/10.1016/j.jag.2024.103723 kostenfrei https://doaj.org/article/a21c4d2d270846d48eea6d98b1491db4 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000773 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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 128 2024 103723-
spelling	10.1016/j.jag.2024.103723 doi (DE-627)DOAJ091688493 (DE-599)DOAJa21c4d2d270846d48eea6d98b1491db4 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Qiqi Zhu verfasserin aut TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios. Target-aware Object tracking Satellite videos Weaken heatmap Pixel-wise refinement Corner-based tracker Physical geography Environmental sciences Xin Huang verfasserin aut Qingfeng Guan verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103723- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103723- https://doi.org/10.1016/j.jag.2024.103723 kostenfrei https://doaj.org/article/a21c4d2d270846d48eea6d98b1491db4 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000773 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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 128 2024 103723-
allfields_unstemmed	10.1016/j.jag.2024.103723 doi (DE-627)DOAJ091688493 (DE-599)DOAJa21c4d2d270846d48eea6d98b1491db4 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Qiqi Zhu verfasserin aut TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios. Target-aware Object tracking Satellite videos Weaken heatmap Pixel-wise refinement Corner-based tracker Physical geography Environmental sciences Xin Huang verfasserin aut Qingfeng Guan verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103723- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103723- https://doi.org/10.1016/j.jag.2024.103723 kostenfrei https://doaj.org/article/a21c4d2d270846d48eea6d98b1491db4 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000773 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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 128 2024 103723-
allfieldsGer	10.1016/j.jag.2024.103723 doi (DE-627)DOAJ091688493 (DE-599)DOAJa21c4d2d270846d48eea6d98b1491db4 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Qiqi Zhu verfasserin aut TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios. Target-aware Object tracking Satellite videos Weaken heatmap Pixel-wise refinement Corner-based tracker Physical geography Environmental sciences Xin Huang verfasserin aut Qingfeng Guan verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103723- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103723- https://doi.org/10.1016/j.jag.2024.103723 kostenfrei https://doaj.org/article/a21c4d2d270846d48eea6d98b1491db4 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000773 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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 128 2024 103723-
allfieldsSound	10.1016/j.jag.2024.103723 doi (DE-627)DOAJ091688493 (DE-599)DOAJa21c4d2d270846d48eea6d98b1491db4 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Qiqi Zhu verfasserin aut TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios. Target-aware Object tracking Satellite videos Weaken heatmap Pixel-wise refinement Corner-based tracker Physical geography Environmental sciences Xin Huang verfasserin aut Qingfeng Guan verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103723- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103723- https://doi.org/10.1016/j.jag.2024.103723 kostenfrei https://doaj.org/article/a21c4d2d270846d48eea6d98b1491db4 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000773 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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 128 2024 103723-
language	English
source	In International Journal of Applied Earth Observations and Geoinformation 128(2024), Seite 103723- volume:128 year:2024 pages:103723-
sourceStr	In International Journal of Applied Earth Observations and Geoinformation 128(2024), Seite 103723- volume:128 year:2024 pages:103723-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Target-aware Object tracking Satellite videos Weaken heatmap Pixel-wise refinement Corner-based tracker Physical geography Environmental sciences
isfreeaccess_bool	true
container_title	International Journal of Applied Earth Observations and Geoinformation
authorswithroles_txt_mv	Qiqi Zhu @@aut@@ Xin Huang @@aut@@ Qingfeng Guan @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	359784119
id	DOAJ091688493
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ091688493</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413235832.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jag.2024.103723</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ091688493</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa21c4d2d270846d48eea6d98b1491db4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GB3-5030</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Qiqi Zhu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Target-aware</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Object tracking</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Satellite videos</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Weaken heatmap</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pixel-wise refinement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Corner-based tracker</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Physical geography</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xin Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Qingfeng Guan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International Journal of Applied Earth Observations and Geoinformation</subfield><subfield code="d">Elsevier, 2022</subfield><subfield code="g">128(2024), Seite 103723-</subfield><subfield code="w">(DE-627)359784119</subfield><subfield code="w">(DE-600)2097960-5</subfield><subfield code="x">1872826X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:128</subfield><subfield code="g">year:2024</subfield><subfield code="g">pages:103723-</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jag.2024.103723</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a21c4d2d270846d48eea6d98b1491db4</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S1569843224000773</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1569-8432</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">128</subfield><subfield code="j">2024</subfield><subfield code="h">103723-</subfield></datafield></record></collection>
callnumber-first	G - Geography, Anthropology, Recreation
author	Qiqi Zhu
spellingShingle	Qiqi Zhu misc GB3-5030 misc GE1-350 misc Target-aware misc Object tracking misc Satellite videos misc Weaken heatmap misc Pixel-wise refinement misc Corner-based tracker misc Physical geography misc Environmental sciences TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos
authorStr	Qiqi Zhu
ppnlink_with_tag_str_mv	@@773@@(DE-627)359784119
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	GB3-5030
illustrated	Not Illustrated
issn	1872826X
topic_title	GB3-5030 GE1-350 TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos Target-aware Object tracking Satellite videos Weaken heatmap Pixel-wise refinement Corner-based tracker
topic	misc GB3-5030 misc GE1-350 misc Target-aware misc Object tracking misc Satellite videos misc Weaken heatmap misc Pixel-wise refinement misc Corner-based tracker misc Physical geography misc Environmental sciences
topic_unstemmed	misc GB3-5030 misc GE1-350 misc Target-aware misc Object tracking misc Satellite videos misc Weaken heatmap misc Pixel-wise refinement misc Corner-based tracker misc Physical geography misc Environmental sciences
topic_browse	misc GB3-5030 misc GE1-350 misc Target-aware misc Object tracking misc Satellite videos misc Weaken heatmap misc Pixel-wise refinement misc Corner-based tracker misc Physical geography misc Environmental sciences
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	International Journal of Applied Earth Observations and Geoinformation
hierarchy_parent_id	359784119
hierarchy_top_title	International Journal of Applied Earth Observations and Geoinformation
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)359784119 (DE-600)2097960-5
title	TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos
ctrlnum	(DE-627)DOAJ091688493 (DE-599)DOAJa21c4d2d270846d48eea6d98b1491db4
title_full	TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos
author_sort	Qiqi Zhu
journal	International Journal of Applied Earth Observations and Geoinformation
journalStr	International Journal of Applied Earth Observations and Geoinformation
callnumber-first-code	G
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2024
contenttype_str_mv	txt
container_start_page	103723
author_browse	Qiqi Zhu Xin Huang Qingfeng Guan
container_volume	128
class	GB3-5030 GE1-350
format_se	Elektronische Aufsätze
author-letter	Qiqi Zhu
doi_str_mv	10.1016/j.jag.2024.103723
author2-role	verfasserin
title_sort	tabctnet: target-aware bilateral cnn-transformer network for single object tracking in satellite videos
callnumber	GB3-5030
title_auth	TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos
abstract	Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios.
abstractGer	Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios.
abstract_unstemmed	Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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
title_short	TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos
url	https://doi.org/10.1016/j.jag.2024.103723 https://doaj.org/article/a21c4d2d270846d48eea6d98b1491db4 http://www.sciencedirect.com/science/article/pii/S1569843224000773 https://doaj.org/toc/1569-8432
remote_bool	true
author2	Xin Huang Qingfeng Guan
author2Str	Xin Huang Qingfeng Guan
ppnlink	359784119
callnumber-subject	GB - Physical Geography
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1016/j.jag.2024.103723
callnumber-a	GB3-5030
up_date	2024-07-03T21:40:40.333Z
_version_	1803595641348161536
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ091688493</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413235832.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jag.2024.103723</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ091688493</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa21c4d2d270846d48eea6d98b1491db4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GB3-5030</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Qiqi Zhu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Target-aware</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Object tracking</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Satellite videos</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Weaken heatmap</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pixel-wise refinement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Corner-based tracker</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Physical geography</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xin Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Qingfeng Guan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International Journal of Applied Earth Observations and Geoinformation</subfield><subfield code="d">Elsevier, 2022</subfield><subfield code="g">128(2024), Seite 103723-</subfield><subfield code="w">(DE-627)359784119</subfield><subfield code="w">(DE-600)2097960-5</subfield><subfield code="x">1872826X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:128</subfield><subfield code="g">year:2024</subfield><subfield code="g">pages:103723-</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jag.2024.103723</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a21c4d2d270846d48eea6d98b1491db4</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S1569843224000773</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1569-8432</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">128</subfield><subfield code="j">2024</subfield><subfield code="h">103723-</subfield></datafield></record></collection>
score	7.3991175

Nicht das Richtige dabei?

Schreiben Sie uns!

TabCtNet: Target-aware bilateral CNN-transformer network for single object tracking in satellite videos

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?