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A visual single-object tracker is an indispensable component of underwatervehicles (UVs) in marine organism grasping tasks. Its accuracy and stabilityare imperative to guide the UVs to perform grasping behavior. Althoughsingle-object trackers show competitive performance in the challenge ofunderwater image degradation, there are still issues with sample imbalance andexclusion of similar objects that need to be addressed for application inmarine 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 isdesigned to train the sample-imbalanced underwater tracker so that the trackeris exposed to a great number of underwater domain training samples and learnsthe feature expressions. The MBPP paradigm is proposed to exclude similarobjects. It uses the estimation box predicted with a Kalman filter and thecandidate boxes in the response map to relocate the lost tracked object in thecandidate 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 ourproposed UOSTrack for UVs in marine organism grasping tasks. More details canbe found at https://github.com/LiYunfengLYF/UOSTrack.

Underwater Object Tracker: UOSTrack for Marine Organism Grasping of Underwater Vehicles