  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.
