水下目标跟踪器：面向水下航行器海洋生物抓取的UOSTrack
Underwater Object Tracker: UOSTrack for Marine Organism Grasping of Underwater Vehicles
AI总结：
针对水下航行器海洋生物抓取中视觉跟踪存在的样本不平衡、相似目标干扰问题，提出融合水下-露天混合训练与运动后处理的UOSTrack跟踪器，在基准测试与实地实验中均实现性能提升。
摘要：
视觉单目标跟踪器是水下航行器（UVs）执行海洋生物抓取任务时不可或缺的组成部分，其精度与稳定性对引导水下航行器完成抓取动作至关重要。尽管单目标跟踪器在应对水下图像退化挑战时已展现出具有竞争力的性能，但在海洋生物抓取的实际应用中，仍存在样本不平衡与相似目标干扰排除的问题有待解决。本文提出了Underwater OSTrack（UOSTrack，水下OSTrack），该方法包含水下图像与露天序列混合训练（UOHT）以及基于运动的后处理（MBPP）两个模块。UOHT训练范式专为解决水下跟踪器样本不平衡问题而设计，能让跟踪器接触大量水下领域训练样本，学习水下场景的特征表达。MBPP范式用于排除相似目标干扰，它利用卡尔曼滤波预测的估计框与响应图中的候选框，在候选区域内重新定位丢失的跟踪目标。在多个基准测试集上，UOSTrack相较于当前最优方法，平均性能提升4.41%，最大提升达7.98%。实地实验验证了本文提出的UOSTrack在水下航行器海洋生物抓取任务中的精度与稳定性。更多细节可访问https://github.com/LiYunfengLYF/UOSTrack查看。
英文摘要：
A visual single-object tracker is an indispensable component of underwater vehicles (UVs) in marine organism grasping tasks. Its accuracy and stability are imperative to guide the UVs to perform grasping behavior. Although single-object trackers show competitive performance in the challenge of underwater image degradation, there are still issues with sample imbalance and exclusion of similar objects that need to be addressed for application in marine organism grasping. This paper proposes Underwater OSTrack (UOSTrack), which consists of underwater image and open-air sequence hybrid training (UOHT), and motion-based post-processing (MBPP). The UOHT training paradigm is designed to train the sample-imbalanced underwater tracker so that the tracker is exposed to a great number of underwater domain training samples and learns the feature expressions. The MBPP paradigm is proposed to exclude similar objects. It uses the estimation box predicted with a Kalman filter and the candidate boxes in the response map to relocate the lost tracked object in the candidate area. UOSTrack achieves an average performance improvement of 4.41% and 7.98% maximum compared to state-of-the-art methods on various benchmarks, respectively. Field experiments have verified the accuracy and stability of our proposed UOSTrack for UVs in marine organism grasping tasks. More details can be found at https://github.com/LiYunfengLYF/UOSTrack.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
