Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking

Yuan, D; Chang, X; Li, Z; He, Z

Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking

Yuan, D Chang, X

Li, Z He, Z

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Transactions on Multimedia Computing, Communications and Applications, 2022, 18, (3)
Issue Date:: 2022-08-01

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Field	Value	Language
dc.contributor.author	Yuan, D
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Li, Z
dc.contributor.author	He, Z
dc.date.accessioned	2023-03-31T04:05:53Z
dc.date.available	2023-03-31T04:05:53Z
dc.date.issued	2022-08-01
dc.identifier.citation	ACM Transactions on Multimedia Computing, Communications and Applications, 2022, 18, (3)
dc.identifier.issn	1551-6857
dc.identifier.issn	1551-6865
dc.identifier.uri	http://hdl.handle.net/10453/168923
dc.description.abstract	Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target-tracking tasks. Different from the target-tracking task in the general scenarios, the target-tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the discriminative correlation filter (DCF)-based tracker has achieved good results in tracking tasks in general scenarios, the boundary effect caused by the dense sampling method will reduce the tracking accuracy, especially in UAV-tracking scenarios. In this work, we propose learning an adaptive spatial-temporal context-aware (ASTCA) model in the DCF-based tracking framework to improve the tracking accuracy and reduce the influence of boundary effect, thereby enabling our tracker to more appropriately handle UAV-tracking tasks. Specifically, our ASTCA model can learn a spatial-temporal context weight, which can precisely distinguish the target and background in the UAV-tracking scenarios. Besides, considering the small target scale and the aerial view in UAV-tracking scenarios, our ASTCA model incorporates spatial context information within the DCF-based tracker, which could effectively alleviate background interference. Extensive experiments demonstrate that our ASTCA method performs favorably against state-of-the-art tracking methods on some standard UAV datasets.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation	http://purl.org/au-research/grants/arc/DE190100626
dc.relation.ispartof	ACM Transactions on Multimedia Computing, Communications and Applications
dc.relation.isbasedon	10.1145/3486678
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-31T04:05:40Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	3

Abstract:

Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target-tracking tasks. Different from the target-tracking task in the general scenarios, the target-tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the discriminative correlation filter (DCF)-based tracker has achieved good results in tracking tasks in general scenarios, the boundary effect caused by the dense sampling method will reduce the tracking accuracy, especially in UAV-tracking scenarios. In this work, we propose learning an adaptive spatial-temporal context-aware (ASTCA) model in the DCF-based tracking framework to improve the tracking accuracy and reduce the influence of boundary effect, thereby enabling our tracker to more appropriately handle UAV-tracking tasks. Specifically, our ASTCA model can learn a spatial-temporal context weight, which can precisely distinguish the target and background in the UAV-tracking scenarios. Besides, considering the small target scale and the aerial view in UAV-tracking scenarios, our ASTCA model incorporates spatial context information within the DCF-based tracker, which could effectively alleviate background interference. Extensive experiments demonstrate that our ASTCA method performs favorably against state-of-the-art tracking methods on some standard UAV datasets.

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http://hdl.handle.net/10453/168923