Adaptive Trajectory Correction for Underwater Object Tracking
Luo, Xudong Yuan, Di Shu, Xiu Liu, Qiao Chang, Xiaojun He, Zhenyu
Luo, Xudong
Yuan, Di
Shu, Xiu
Liu, Qiao
Chang, Xiaojun
He, Zhenyu
Supervisor
Department
Computer Vision
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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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.
Citation
X. Luo, D. Yuan, X. Shu, Q. Liu, X. Chang and Z. He, "Adaptive Trajectory Correction for Underwater Object Tracking," in IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2025.3544750
Source
IEEE Transactions on Circuits and Systems for Video Technology
Conference
Keywords
Target tracking, Trajectory, Image enhancement, Training, Object tracking, Image quality, Circuits, systems, Visualization, Interference, Degradation
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Source
Publisher
IEEE