  Vision camera and sonar are naturally complementary in the underwater
environment. Combining the information from two modalities will promote better
observation of underwater targets. However, this problem has not received
sufficient attention in previous research. Therefore, this paper introduces a
new challenging RGB-Sonar (RGB-S) tracking task and investigates how to achieve
efficient tracking of an underwater target through the interaction of RGB and
sonar modalities. Specifically, we first propose an RGBS50 benchmark dataset
containing 50 sequences and more than 87000 high-quality annotated bounding
boxes. Experimental results show that the RGBS50 benchmark poses a challenge to
currently popular SOT trackers. Second, we propose an RGB-S tracker called
SCANet, which includes a spatial cross-attention module (SCAM) consisting of a
novel spatial cross-attention layer and two independent global integration
modules. The spatial cross-attention is used to overcome the problem of spatial
misalignment of between RGB and sonar images. Third, we propose a SOT
data-based RGB-S simulation training method (SRST) to overcome the lack of
RGB-S training datasets. It converts RGB images into sonar-like saliency images
to construct pseudo-data pairs, enabling the model to learn the semantic
structure of RGB-S-like data. Comprehensive experiments show that the proposed
spatial cross-attention effectively achieves the interaction between RGB and
sonar modalities and SCANet achieves state-of-the-art performance on the
proposed benchmark. The code is available at
https://github.com/LiYunfengLYF/RGBS50.
