水下无人系统学报2025，Vol.33Issue(2)：212-219,290,9.DOI:10.11993/j.issn.2096-3920.2025-0007
基于场景感知的水下视觉目标跟踪方法
Underwater Visual Object Tracking Method Based on Scene Perception
摘要
Abstract
Underwater visual object tracking is a core technology for scene understanding in autonomous undersea vehicle(AUV)systems.However,challenges such as uneven illumination,background interference,and target appearance variation in complex underwater environments severely affect the accuracy and stability of traditional visual tracking methods.Existing approaches primarily rely on the appearance modeling of the target,making them unreliable in complex environments,particularly when similar targets are present,leading to misidentification and tracking drift.This paper proposed an underwater single-object tracking method based on scene perception that utilized a regional segmentation-based graph convolution module to extract all target regions in the scene.By leveraging a graph convolutional network,the proposed method captured long-range dependencies between the target region and surrounding key regions,significantly enhancing the discrimination capability against similar targets.Additionally,a dual-view graph contrastive learning strategy was introduced,which enabled unsupervised online updates for the graph convolution module by generating randomly perturbed target feature views,ensuring strong adaptability and stability of the model in complex environments.Experiments show that the proposed method is significantly better than the classical method in terms of tracking accuracy and robustness,especially in scenes with large lighting changes,complex backgrounds,and strong interference of similar targets,and the success rate and accuracy are significantly improved.These results indicate that the proposed method effectively addresses target drift challenges in underwater object tracking caused by illumination variations and background interference,maintaining stable tracking even in the presence of similar targets,thus providing an efficient and reliable tracking solution for underwater unmanned systems.关键词
水下视觉/目标跟踪/场景感知/图卷积网络/双视图图对比学习Key words
underwater visual/object tracking/scene perception/graph convolutional network/dual-view graph contrastive learning分类
武器工业引用本文复制引用
胡千伟,王代维,李人杰,俞晓帆,康彬,苏偌宇..基于场景感知的水下视觉目标跟踪方法[J].水下无人系统学报,2025,33(2):212-219,290,9.基金项目
国家自然科学基金项目资助(62171232) （62171232）
水声通信与海洋信息技术教育部重点实验室(厦门大学)开放课题(UAC202301). （厦门大学）
