Adaptive Trajectory Correction for Underwater Object Tracking

Luo, X; Yuan, D; Shu, X; Liu, Q; Chang, X; He, Z

Adaptive Trajectory Correction for Underwater Object Tracking

Luo, X Yuan, D Shu, X Liu, Q Chang, X

He, Z

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems for Video Technology, 2025, PP, (99), pp. 1-1
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Luo, X
dc.contributor.author	Yuan, D
dc.contributor.author	Shu, X
dc.contributor.author	Liu, Q
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	He, Z
dc.date.accessioned	2025-07-21T09:58:01Z
dc.date.available	2025-07-21T09:58:01Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Transactions on Circuits and Systems for Video Technology, 2025, PP, (99), pp. 1-1
dc.identifier.issn	1051-8215
dc.identifier.issn	1558-2205
dc.identifier.uri	http://hdl.handle.net/10453/188596
dc.description.abstract	Most extant underwater object tracking (UOT) utilize generic tracking algorithms, which lack applicability to underwater tracking scenarios. Moreover, these algorithms primarily emphasize minimizing interference from various challenging tasks to prevent target drift, but pay less attention to the strategies for mitigating target drift once it occurs. To alleviate the above problems, we propose a simple, effective, and UOT-focused adaptive trajectory correction framework, named ATCTrack. From the perspective of tracking failure, this methodology aims to promptly identify and rectify unreasonable target drift through accurate trajectory coordinate correction and trajectory template updates. Additionally, to mitigate the adverse effects of potential erroneous corrections, we implement an adaptive strategy that corrects only significant target drift, allowing for self-correction within a certain margin. Finally, we introduce an adaptive underwater image enhancement technique to improve the underwater image quality and maintain the trajectory's stability and clarity. Our tracker achieves state-of-the-art performance on the currently prevalent UOT tracking benchmarks compared to other trackers.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Circuits and Systems for Video Technology
dc.relation.isbasedon	10.1109/TCSVT.2025.3544750
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	Adaptive Trajectory Correction for Underwater Object Tracking
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	closed_access	*
dc.date.updated	2025-07-21T09:57:58Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Most extant underwater object tracking (UOT) utilize generic tracking algorithms, which lack applicability to underwater tracking scenarios. Moreover, these algorithms primarily emphasize minimizing interference from various challenging tasks to prevent target drift, but pay less attention to the strategies for mitigating target drift once it occurs. To alleviate the above problems, we propose a simple, effective, and UOT-focused adaptive trajectory correction framework, named ATCTrack. From the perspective of tracking failure, this methodology aims to promptly identify and rectify unreasonable target drift through accurate trajectory coordinate correction and trajectory template updates. Additionally, to mitigate the adverse effects of potential erroneous corrections, we implement an adaptive strategy that corrects only significant target drift, allowing for self-correction within a certain margin. Finally, we introduce an adaptive underwater image enhancement technique to improve the underwater image quality and maintain the trajectory's stability and clarity. Our tracker achieves state-of-the-art performance on the currently prevalent UOT tracking benchmarks compared to other trackers.

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http://hdl.handle.net/10453/188596